{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_housing_data(housing_path = './'):\n",
    "    csv_path = os.path.join(housing_path, 'housing.csv')\n",
    "    return pd.read_csv(csv_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing=load_housing_data()\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20640, 10)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.shape#展示行列信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      "longitude             20640 non-null float64\n",
      "latitude              20640 non-null float64\n",
      "housing_median_age    20640 non-null float64\n",
      "total_rooms           20640 non-null float64\n",
      "total_bedrooms        20433 non-null float64\n",
      "population            20640 non-null float64\n",
      "households            20640 non-null float64\n",
      "median_income         20640 non-null float64\n",
      "median_house_value    20640 non-null float64\n",
      "ocean_proximity       20640 non-null object\n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info() #查看每列数据的数据类型信息,得到每个属性类型和非空值数据量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1H OCEAN     9136\n",
       "INLAND        6551\n",
       "NEAR OCEAN    2658\n",
       "NEAR BAY      2290\n",
       "ISLAND           5\n",
       "Name: ocean_proximity, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"ocean_proximity\"].value_counts()#得到ocean_proximity的值的种类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
       "std        2.003532      2.135952           12.585558   2181.615252   \n",
       "min     -124.350000     32.540000            1.000000      2.000000   \n",
       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
       "std        421.385070   1132.462122    382.329753       1.899822   \n",
       "min          1.000000      3.000000      1.000000       0.499900   \n",
       "25%        296.000000    787.000000    280.000000       2.563400   \n",
       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
       "\n",
       "       median_house_value  \n",
       "count        20640.000000  \n",
       "mean        206855.816909  \n",
       "std         115395.615874  \n",
       "min          14999.000000  \n",
       "25%         119600.000000  \n",
       "50%         179700.000000  \n",
       "75%         264725.000000  \n",
       "max         500001.000000  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()#显示数据的属性摘要，各列的平均值，最小值，"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "e:\\python3.7\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\tools.py:307: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "e:\\python3.7\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\tools.py:307: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "e:\\python3.7\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\tools.py:313: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n",
      "e:\\python3.7\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\tools.py:313: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "housing.hist(bins = 50, figsize = (20, 15))#直方图\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 房龄和房价被设定了上限\n",
    "   1. 设置了上限的区域，重新收集标签值\n",
    "   2. 把设置了上限的区域数据移除"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 重尾 头高尾巴长\n",
    "    1.转换数据，把数据形状变成正太分布形式"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 收入特征\n",
    "数据提供的上游证实 是年薪 单位万美元， 提前对特征进行缩放是正常的，需要得知数据是如何缩放的"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 创建训练集和测试集\n",
    " 训练集用于模型训练 占整个数据集的80% ，测试占20%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4128"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "def split_train_test(data, test_radio):\n",
    "    np.random.seed(42)\n",
    "    indices = np.random.permutation(len(data))  # 对原来的数组进行重新洗牌，随机打乱原来的元素顺序\n",
    "    test_set_size = int(len(data) * test_radio)\n",
    "    test_indices = indices[:test_set_size]\n",
    "    train_indices = indices[test_set_size:]\n",
    "    return data.iloc[train_indices],  data.iloc[test_indices]\n",
    "train_set, test_set =  split_train_test(housing, 0.2)\n",
    "len(test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16512"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 对样本设置唯一标志符\n",
    "\n",
    "对标识符取hash值， 去hash的最后一个字节， 值小于等于51 ，256*20%，放入测试集\n",
    "\n",
    "hash值相同，对象不一定相同，hash不同，对象一定不同 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import hashlib\n",
    "def test_set_check(identifier,test_radio,hash=hashlib.md5):\n",
    "    return hash(np.int64(identifier)).digest()[-1]<256*test_radio  #digest()返回摘要，作为二进制数据字符串"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_train_test_by_id(data,test_radio,id_column):\n",
    "    ids=data[id_column]\n",
    "    in_test_set=ids.apply(lambda id_:test_set_check(id_,test_radio))\n",
    "    return data.loc[~in_test_set],data.loc[in_test_set]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0      0    -122.23     37.88                41.0        880.0   \n",
       "1      1    -122.22     37.86                21.0       7099.0   \n",
       "2      2    -122.24     37.85                52.0       1467.0   \n",
       "3      3    -122.25     37.85                52.0       1274.0   \n",
       "4      4    -122.25     37.85                52.0       1627.0   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0           129.0       322.0       126.0         8.3252            452600.0   \n",
       "1          1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2           190.0       496.0       177.0         7.2574            352100.0   \n",
       "3           235.0       558.0       219.0         5.6431            341300.0   \n",
       "4           280.0       565.0       259.0         3.8462            342200.0   \n",
       "\n",
       "  ocean_proximity  \n",
       "0        NEAR BAY  \n",
       "1        NEAR BAY  \n",
       "2        NEAR BAY  \n",
       "3        NEAR BAY  \n",
       "4        NEAR BAY  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_with_id=housing.reset_index()#使用行所有作为标志符ID\n",
    "housing_with_id.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set,test_set=split_train_test_by_id(housing_with_id,0.2,\"index\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2535.0</td>\n",
       "      <td>489.0</td>\n",
       "      <td>1094.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>3.6591</td>\n",
       "      <td>299200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0      0    -122.23     37.88                41.0        880.0   \n",
       "1      1    -122.22     37.86                21.0       7099.0   \n",
       "2      2    -122.24     37.85                52.0       1467.0   \n",
       "3      3    -122.25     37.85                52.0       1274.0   \n",
       "6      6    -122.25     37.84                52.0       2535.0   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0           129.0       322.0       126.0         8.3252            452600.0   \n",
       "1          1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2           190.0       496.0       177.0         7.2574            352100.0   \n",
       "3           235.0       558.0       219.0         5.6431            341300.0   \n",
       "6           489.0      1094.0       514.0         3.6591            299200.0   \n",
       "\n",
       "  ocean_proximity  \n",
       "0        NEAR BAY  \n",
       "1        NEAR BAY  \n",
       "2        NEAR BAY  \n",
       "3        NEAR BAY  \n",
       "6        NEAR BAY  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 基于行索引作为唯一 标志符的话，不能插入也不能删除数据，只有在数据尾部插入，否则索引会改变"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#寻找唯一索引来创建唯一标志符\n",
    "#经纬度组合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_with_id[\"id\"]=housing[\"longitude\"]*1000+housing[\"latitude\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122192.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122182.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122202.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>919.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>4.0368</td>\n",
       "      <td>269700.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2535.0</td>\n",
       "      <td>489.0</td>\n",
       "      <td>1094.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>3.6591</td>\n",
       "      <td>299200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3104.0</td>\n",
       "      <td>687.0</td>\n",
       "      <td>1157.0</td>\n",
       "      <td>647.0</td>\n",
       "      <td>3.1200</td>\n",
       "      <td>241400.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.84</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2555.0</td>\n",
       "      <td>665.0</td>\n",
       "      <td>1206.0</td>\n",
       "      <td>595.0</td>\n",
       "      <td>2.0804</td>\n",
       "      <td>226700.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122222.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3549.0</td>\n",
       "      <td>707.0</td>\n",
       "      <td>1551.0</td>\n",
       "      <td>714.0</td>\n",
       "      <td>3.6912</td>\n",
       "      <td>261100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2202.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>910.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>3.2031</td>\n",
       "      <td>281500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122222.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3503.0</td>\n",
       "      <td>752.0</td>\n",
       "      <td>1504.0</td>\n",
       "      <td>734.0</td>\n",
       "      <td>3.2705</td>\n",
       "      <td>241800.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122222.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2491.0</td>\n",
       "      <td>474.0</td>\n",
       "      <td>1098.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>3.0750</td>\n",
       "      <td>213500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122222.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>696.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>2.6736</td>\n",
       "      <td>191300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122222.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2643.0</td>\n",
       "      <td>626.0</td>\n",
       "      <td>1212.0</td>\n",
       "      <td>620.0</td>\n",
       "      <td>1.9167</td>\n",
       "      <td>159200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122222.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.85</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1120.0</td>\n",
       "      <td>283.0</td>\n",
       "      <td>697.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>2.1250</td>\n",
       "      <td>140000.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122222.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>347.0</td>\n",
       "      <td>793.0</td>\n",
       "      <td>331.0</td>\n",
       "      <td>2.7750</td>\n",
       "      <td>152500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122232.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1228.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>648.0</td>\n",
       "      <td>303.0</td>\n",
       "      <td>2.1202</td>\n",
       "      <td>155500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122232.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.84</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2239.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>990.0</td>\n",
       "      <td>419.0</td>\n",
       "      <td>1.9911</td>\n",
       "      <td>158700.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122222.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1503.0</td>\n",
       "      <td>298.0</td>\n",
       "      <td>690.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>2.6033</td>\n",
       "      <td>162900.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122232.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0       0    -122.23     37.88                41.0        880.0   \n",
       "1       1    -122.22     37.86                21.0       7099.0   \n",
       "2       2    -122.24     37.85                52.0       1467.0   \n",
       "3       3    -122.25     37.85                52.0       1274.0   \n",
       "4       4    -122.25     37.85                52.0       1627.0   \n",
       "5       5    -122.25     37.85                52.0        919.0   \n",
       "6       6    -122.25     37.84                52.0       2535.0   \n",
       "7       7    -122.25     37.84                52.0       3104.0   \n",
       "8       8    -122.26     37.84                42.0       2555.0   \n",
       "9       9    -122.25     37.84                52.0       3549.0   \n",
       "10     10    -122.26     37.85                52.0       2202.0   \n",
       "11     11    -122.26     37.85                52.0       3503.0   \n",
       "12     12    -122.26     37.85                52.0       2491.0   \n",
       "13     13    -122.26     37.84                52.0        696.0   \n",
       "14     14    -122.26     37.85                52.0       2643.0   \n",
       "15     15    -122.26     37.85                50.0       1120.0   \n",
       "16     16    -122.27     37.85                52.0       1966.0   \n",
       "17     17    -122.27     37.85                52.0       1228.0   \n",
       "18     18    -122.26     37.84                50.0       2239.0   \n",
       "19     19    -122.27     37.84                52.0       1503.0   \n",
       "\n",
       "    total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0            129.0       322.0       126.0         8.3252            452600.0   \n",
       "1           1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2            190.0       496.0       177.0         7.2574            352100.0   \n",
       "3            235.0       558.0       219.0         5.6431            341300.0   \n",
       "4            280.0       565.0       259.0         3.8462            342200.0   \n",
       "5            213.0       413.0       193.0         4.0368            269700.0   \n",
       "6            489.0      1094.0       514.0         3.6591            299200.0   \n",
       "7            687.0      1157.0       647.0         3.1200            241400.0   \n",
       "8            665.0      1206.0       595.0         2.0804            226700.0   \n",
       "9            707.0      1551.0       714.0         3.6912            261100.0   \n",
       "10           434.0       910.0       402.0         3.2031            281500.0   \n",
       "11           752.0      1504.0       734.0         3.2705            241800.0   \n",
       "12           474.0      1098.0       468.0         3.0750            213500.0   \n",
       "13           191.0       345.0       174.0         2.6736            191300.0   \n",
       "14           626.0      1212.0       620.0         1.9167            159200.0   \n",
       "15           283.0       697.0       264.0         2.1250            140000.0   \n",
       "16           347.0       793.0       331.0         2.7750            152500.0   \n",
       "17           293.0       648.0       303.0         2.1202            155500.0   \n",
       "18           455.0       990.0       419.0         1.9911            158700.0   \n",
       "19           298.0       690.0       275.0         2.6033            162900.0   \n",
       "\n",
       "   ocean_proximity         id  \n",
       "0         NEAR BAY -122192.12  \n",
       "1         NEAR BAY -122182.14  \n",
       "2         NEAR BAY -122202.15  \n",
       "3         NEAR BAY -122212.15  \n",
       "4         NEAR BAY -122212.15  \n",
       "5         NEAR BAY -122212.15  \n",
       "6         NEAR BAY -122212.16  \n",
       "7         NEAR BAY -122212.16  \n",
       "8         NEAR BAY -122222.16  \n",
       "9         NEAR BAY -122212.16  \n",
       "10        NEAR BAY -122222.15  \n",
       "11        NEAR BAY -122222.15  \n",
       "12        NEAR BAY -122222.15  \n",
       "13        NEAR BAY -122222.16  \n",
       "14        NEAR BAY -122222.15  \n",
       "15        NEAR BAY -122222.15  \n",
       "16        NEAR BAY -122232.15  \n",
       "17        NEAR BAY -122232.15  \n",
       "18        NEAR BAY -122222.16  \n",
       "19        NEAR BAY -122232.16  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_with_id.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set,test_set=split_train_test_by_id(housing_with_id,0.2,\"id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122192.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122182.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122202.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>919.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>4.0368</td>\n",
       "      <td>269700.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2535.0</td>\n",
       "      <td>489.0</td>\n",
       "      <td>1094.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>3.6591</td>\n",
       "      <td>299200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3104.0</td>\n",
       "      <td>687.0</td>\n",
       "      <td>1157.0</td>\n",
       "      <td>647.0</td>\n",
       "      <td>3.1200</td>\n",
       "      <td>241400.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3549.0</td>\n",
       "      <td>707.0</td>\n",
       "      <td>1551.0</td>\n",
       "      <td>714.0</td>\n",
       "      <td>3.6912</td>\n",
       "      <td>261100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>347.0</td>\n",
       "      <td>793.0</td>\n",
       "      <td>331.0</td>\n",
       "      <td>2.7750</td>\n",
       "      <td>152500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122232.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1228.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>648.0</td>\n",
       "      <td>303.0</td>\n",
       "      <td>2.1202</td>\n",
       "      <td>155500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122232.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1503.0</td>\n",
       "      <td>298.0</td>\n",
       "      <td>690.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>2.6033</td>\n",
       "      <td>162900.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122232.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.85</td>\n",
       "      <td>40.0</td>\n",
       "      <td>751.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>1.3578</td>\n",
       "      <td>147500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122232.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.85</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1639.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>929.0</td>\n",
       "      <td>366.0</td>\n",
       "      <td>1.7135</td>\n",
       "      <td>159800.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122232.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2436.0</td>\n",
       "      <td>541.0</td>\n",
       "      <td>1015.0</td>\n",
       "      <td>478.0</td>\n",
       "      <td>1.7250</td>\n",
       "      <td>113900.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122232.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1688.0</td>\n",
       "      <td>337.0</td>\n",
       "      <td>853.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>2.1806</td>\n",
       "      <td>99700.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122232.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2224.0</td>\n",
       "      <td>437.0</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>422.0</td>\n",
       "      <td>2.6000</td>\n",
       "      <td>132600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122232.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>-122.28</td>\n",
       "      <td>37.85</td>\n",
       "      <td>41.0</td>\n",
       "      <td>535.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>2.4038</td>\n",
       "      <td>107500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122242.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>-122.28</td>\n",
       "      <td>37.85</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1130.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>607.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>2.4597</td>\n",
       "      <td>93800.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122242.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>-122.28</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1898.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>1102.0</td>\n",
       "      <td>397.0</td>\n",
       "      <td>1.8080</td>\n",
       "      <td>105500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122242.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0       0    -122.23     37.88                41.0        880.0   \n",
       "1       1    -122.22     37.86                21.0       7099.0   \n",
       "2       2    -122.24     37.85                52.0       1467.0   \n",
       "3       3    -122.25     37.85                52.0       1274.0   \n",
       "4       4    -122.25     37.85                52.0       1627.0   \n",
       "5       5    -122.25     37.85                52.0        919.0   \n",
       "6       6    -122.25     37.84                52.0       2535.0   \n",
       "7       7    -122.25     37.84                52.0       3104.0   \n",
       "9       9    -122.25     37.84                52.0       3549.0   \n",
       "16     16    -122.27     37.85                52.0       1966.0   \n",
       "17     17    -122.27     37.85                52.0       1228.0   \n",
       "19     19    -122.27     37.84                52.0       1503.0   \n",
       "20     20    -122.27     37.85                40.0        751.0   \n",
       "21     21    -122.27     37.85                42.0       1639.0   \n",
       "22     22    -122.27     37.84                52.0       2436.0   \n",
       "23     23    -122.27     37.84                52.0       1688.0   \n",
       "24     24    -122.27     37.84                52.0       2224.0   \n",
       "25     25    -122.28     37.85                41.0        535.0   \n",
       "26     26    -122.28     37.85                49.0       1130.0   \n",
       "27     27    -122.28     37.85                52.0       1898.0   \n",
       "\n",
       "    total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0            129.0       322.0       126.0         8.3252            452600.0   \n",
       "1           1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2            190.0       496.0       177.0         7.2574            352100.0   \n",
       "3            235.0       558.0       219.0         5.6431            341300.0   \n",
       "4            280.0       565.0       259.0         3.8462            342200.0   \n",
       "5            213.0       413.0       193.0         4.0368            269700.0   \n",
       "6            489.0      1094.0       514.0         3.6591            299200.0   \n",
       "7            687.0      1157.0       647.0         3.1200            241400.0   \n",
       "9            707.0      1551.0       714.0         3.6912            261100.0   \n",
       "16           347.0       793.0       331.0         2.7750            152500.0   \n",
       "17           293.0       648.0       303.0         2.1202            155500.0   \n",
       "19           298.0       690.0       275.0         2.6033            162900.0   \n",
       "20           184.0       409.0       166.0         1.3578            147500.0   \n",
       "21           367.0       929.0       366.0         1.7135            159800.0   \n",
       "22           541.0      1015.0       478.0         1.7250            113900.0   \n",
       "23           337.0       853.0       325.0         2.1806             99700.0   \n",
       "24           437.0      1006.0       422.0         2.6000            132600.0   \n",
       "25           123.0       317.0       119.0         2.4038            107500.0   \n",
       "26           244.0       607.0       239.0         2.4597             93800.0   \n",
       "27           421.0      1102.0       397.0         1.8080            105500.0   \n",
       "\n",
       "   ocean_proximity         id  \n",
       "0         NEAR BAY -122192.12  \n",
       "1         NEAR BAY -122182.14  \n",
       "2         NEAR BAY -122202.15  \n",
       "3         NEAR BAY -122212.15  \n",
       "4         NEAR BAY -122212.15  \n",
       "5         NEAR BAY -122212.15  \n",
       "6         NEAR BAY -122212.16  \n",
       "7         NEAR BAY -122212.16  \n",
       "9         NEAR BAY -122212.16  \n",
       "16        NEAR BAY -122232.15  \n",
       "17        NEAR BAY -122232.15  \n",
       "19        NEAR BAY -122232.16  \n",
       "20        NEAR BAY -122232.15  \n",
       "21        NEAR BAY -122232.15  \n",
       "22        NEAR BAY -122232.16  \n",
       "23        NEAR BAY -122232.16  \n",
       "24        NEAR BAY -122232.16  \n",
       "25        NEAR BAY -122242.15  \n",
       "26        NEAR BAY -122242.15  \n",
       "27        NEAR BAY -122242.15  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_set,test_set=train_test_split(housing,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>14196</td>\n",
       "      <td>-117.03</td>\n",
       "      <td>32.71</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3126.0</td>\n",
       "      <td>627.0</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>623.0</td>\n",
       "      <td>3.2596</td>\n",
       "      <td>103000.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8267</td>\n",
       "      <td>-118.16</td>\n",
       "      <td>33.77</td>\n",
       "      <td>49.0</td>\n",
       "      <td>3382.0</td>\n",
       "      <td>787.0</td>\n",
       "      <td>1314.0</td>\n",
       "      <td>756.0</td>\n",
       "      <td>3.8125</td>\n",
       "      <td>382100.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17445</td>\n",
       "      <td>-120.48</td>\n",
       "      <td>34.66</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1897.0</td>\n",
       "      <td>331.0</td>\n",
       "      <td>915.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>4.1563</td>\n",
       "      <td>172600.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14265</td>\n",
       "      <td>-117.11</td>\n",
       "      <td>32.69</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1421.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>1418.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>1.9425</td>\n",
       "      <td>93400.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2271</td>\n",
       "      <td>-119.80</td>\n",
       "      <td>36.78</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2382.0</td>\n",
       "      <td>431.0</td>\n",
       "      <td>874.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>3.5542</td>\n",
       "      <td>96500.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17848</td>\n",
       "      <td>-121.86</td>\n",
       "      <td>37.42</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5032.0</td>\n",
       "      <td>808.0</td>\n",
       "      <td>2695.0</td>\n",
       "      <td>801.0</td>\n",
       "      <td>6.6227</td>\n",
       "      <td>264800.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6252</td>\n",
       "      <td>-117.97</td>\n",
       "      <td>34.04</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1686.0</td>\n",
       "      <td>417.0</td>\n",
       "      <td>1355.0</td>\n",
       "      <td>388.0</td>\n",
       "      <td>2.5192</td>\n",
       "      <td>157300.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9389</td>\n",
       "      <td>-122.53</td>\n",
       "      <td>37.91</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2524.0</td>\n",
       "      <td>398.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>417.0</td>\n",
       "      <td>7.9892</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6113</td>\n",
       "      <td>-117.90</td>\n",
       "      <td>34.13</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1126.0</td>\n",
       "      <td>316.0</td>\n",
       "      <td>819.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>1.5000</td>\n",
       "      <td>139800.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6061</td>\n",
       "      <td>-117.79</td>\n",
       "      <td>34.02</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18690.0</td>\n",
       "      <td>2862.0</td>\n",
       "      <td>9427.0</td>\n",
       "      <td>2777.0</td>\n",
       "      <td>6.4266</td>\n",
       "      <td>315600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16030</td>\n",
       "      <td>-122.45</td>\n",
       "      <td>37.72</td>\n",
       "      <td>52.0</td>\n",
       "      <td>982.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>653.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>4.2167</td>\n",
       "      <td>231900.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8364</td>\n",
       "      <td>-118.35</td>\n",
       "      <td>33.97</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1864.0</td>\n",
       "      <td>616.0</td>\n",
       "      <td>1710.0</td>\n",
       "      <td>575.0</td>\n",
       "      <td>2.2303</td>\n",
       "      <td>159400.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9448</td>\n",
       "      <td>-119.72</td>\n",
       "      <td>37.46</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>2.8750</td>\n",
       "      <td>96000.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17529</td>\n",
       "      <td>-121.88</td>\n",
       "      <td>37.33</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1904.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>3561.0</td>\n",
       "      <td>632.0</td>\n",
       "      <td>2.0972</td>\n",
       "      <td>187500.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5865</td>\n",
       "      <td>-118.34</td>\n",
       "      <td>34.18</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1393.0</td>\n",
       "      <td>301.0</td>\n",
       "      <td>714.0</td>\n",
       "      <td>295.0</td>\n",
       "      <td>2.8125</td>\n",
       "      <td>229900.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7615</td>\n",
       "      <td>-118.23</td>\n",
       "      <td>33.89</td>\n",
       "      <td>36.0</td>\n",
       "      <td>2598.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>1872.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>3.1667</td>\n",
       "      <td>117700.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9707</td>\n",
       "      <td>-121.65</td>\n",
       "      <td>36.67</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2351.0</td>\n",
       "      <td>459.0</td>\n",
       "      <td>1169.0</td>\n",
       "      <td>439.0</td>\n",
       "      <td>2.8924</td>\n",
       "      <td>169600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16332</td>\n",
       "      <td>-121.34</td>\n",
       "      <td>38.03</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4213.0</td>\n",
       "      <td>751.0</td>\n",
       "      <td>2071.0</td>\n",
       "      <td>714.0</td>\n",
       "      <td>4.4063</td>\n",
       "      <td>130800.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3449</td>\n",
       "      <td>-118.43</td>\n",
       "      <td>34.32</td>\n",
       "      <td>34.0</td>\n",
       "      <td>2657.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>1948.0</td>\n",
       "      <td>532.0</td>\n",
       "      <td>4.2330</td>\n",
       "      <td>157400.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5199</td>\n",
       "      <td>-118.28</td>\n",
       "      <td>33.93</td>\n",
       "      <td>21.0</td>\n",
       "      <td>847.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>1283.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>1.4329</td>\n",
       "      <td>94100.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "14196    -117.03     32.71                33.0       3126.0           627.0   \n",
       "8267     -118.16     33.77                49.0       3382.0           787.0   \n",
       "17445    -120.48     34.66                 4.0       1897.0           331.0   \n",
       "14265    -117.11     32.69                36.0       1421.0           367.0   \n",
       "2271     -119.80     36.78                43.0       2382.0           431.0   \n",
       "17848    -121.86     37.42                20.0       5032.0           808.0   \n",
       "6252     -117.97     34.04                28.0       1686.0           417.0   \n",
       "9389     -122.53     37.91                37.0       2524.0           398.0   \n",
       "6113     -117.90     34.13                 5.0       1126.0           316.0   \n",
       "6061     -117.79     34.02                 5.0      18690.0          2862.0   \n",
       "16030    -122.45     37.72                52.0        982.0           197.0   \n",
       "8364     -118.35     33.97                25.0       1864.0           616.0   \n",
       "9448     -119.72     37.46                13.0       1999.0           375.0   \n",
       "17529    -121.88     37.33                36.0       1904.0           689.0   \n",
       "5865     -118.34     34.18                46.0       1393.0           301.0   \n",
       "7615     -118.23     33.89                36.0       2598.0           514.0   \n",
       "9707     -121.65     36.67                52.0       2351.0           459.0   \n",
       "16332    -121.34     38.03                20.0       4213.0           751.0   \n",
       "3449     -118.43     34.32                34.0       2657.0           515.0   \n",
       "5199     -118.28     33.93                21.0        847.0           278.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "14196      2300.0       623.0         3.2596            103000.0   \n",
       "8267       1314.0       756.0         3.8125            382100.0   \n",
       "17445       915.0       336.0         4.1563            172600.0   \n",
       "14265      1418.0       355.0         1.9425             93400.0   \n",
       "2271        874.0       380.0         3.5542             96500.0   \n",
       "17848      2695.0       801.0         6.6227            264800.0   \n",
       "6252       1355.0       388.0         2.5192            157300.0   \n",
       "9389        999.0       417.0         7.9892            500001.0   \n",
       "6113        819.0       311.0         1.5000            139800.0   \n",
       "6061       9427.0      2777.0         6.4266            315600.0   \n",
       "16030       653.0       171.0         4.2167            231900.0   \n",
       "8364       1710.0       575.0         2.2303            159400.0   \n",
       "9448        750.0       308.0         2.8750             96000.0   \n",
       "17529      3561.0       632.0         2.0972            187500.0   \n",
       "5865        714.0       295.0         2.8125            229900.0   \n",
       "7615       1872.0       514.0         3.1667            117700.0   \n",
       "9707       1169.0       439.0         2.8924            169600.0   \n",
       "16332      2071.0       714.0         4.4063            130800.0   \n",
       "3449       1948.0       532.0         4.2330            157400.0   \n",
       "5199       1283.0       277.0         1.4329             94100.0   \n",
       "\n",
       "      ocean_proximity  \n",
       "14196      NEAR OCEAN  \n",
       "8267       NEAR OCEAN  \n",
       "17445      NEAR OCEAN  \n",
       "14265      NEAR OCEAN  \n",
       "2271           INLAND  \n",
       "17848       <1H OCEAN  \n",
       "6252        <1H OCEAN  \n",
       "9389         NEAR BAY  \n",
       "6113        <1H OCEAN  \n",
       "6061        <1H OCEAN  \n",
       "16030        NEAR BAY  \n",
       "8364        <1H OCEAN  \n",
       "9448           INLAND  \n",
       "17529       <1H OCEAN  \n",
       "5865        <1H OCEAN  \n",
       "7615        <1H OCEAN  \n",
       "9707        <1H OCEAN  \n",
       "16332          INLAND  \n",
       "3449        <1H OCEAN  \n",
       "5199        <1H OCEAN  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of        longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "14196    -117.03     32.71                33.0       3126.0           627.0   \n",
       "8267     -118.16     33.77                49.0       3382.0           787.0   \n",
       "17445    -120.48     34.66                 4.0       1897.0           331.0   \n",
       "14265    -117.11     32.69                36.0       1421.0           367.0   \n",
       "2271     -119.80     36.78                43.0       2382.0           431.0   \n",
       "...          ...       ...                 ...          ...             ...   \n",
       "11284    -117.96     33.78                35.0       1330.0           201.0   \n",
       "11964    -117.43     34.02                33.0       3084.0           570.0   \n",
       "5390     -118.38     34.03                36.0       2101.0           569.0   \n",
       "860      -121.96     37.58                15.0       3575.0           597.0   \n",
       "15795    -122.42     37.77                52.0       4226.0          1315.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "14196      2300.0       623.0         3.2596            103000.0   \n",
       "8267       1314.0       756.0         3.8125            382100.0   \n",
       "17445       915.0       336.0         4.1563            172600.0   \n",
       "14265      1418.0       355.0         1.9425             93400.0   \n",
       "2271        874.0       380.0         3.5542             96500.0   \n",
       "...           ...         ...            ...                 ...   \n",
       "11284       658.0       217.0         6.3700            229200.0   \n",
       "11964      1753.0       449.0         3.0500             97800.0   \n",
       "5390       1756.0       527.0         2.9344            222100.0   \n",
       "860        1777.0       559.0         5.7192            283500.0   \n",
       "15795      2619.0      1242.0         2.5755            325000.0   \n",
       "\n",
       "      ocean_proximity  \n",
       "14196      NEAR OCEAN  \n",
       "8267       NEAR OCEAN  \n",
       "17445      NEAR OCEAN  \n",
       "14265      NEAR OCEAN  \n",
       "2271           INLAND  \n",
       "...               ...  \n",
       "11284       <1H OCEAN  \n",
       "11964          INLAND  \n",
       "5390        <1H OCEAN  \n",
       "860         <1H OCEAN  \n",
       "15795        NEAR BAY  \n",
       "\n",
       "[16512 rows x 10 columns]>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 从数据可视化中探索数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分层采用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 抽样偏差：调查公司给1000个人来调研几个问题，不会在电话簿中随机查找1000人，他们视图确保1000人代表全体人口，美国人，51.3女性，48.7男性\n",
    "# 你的调查应该维持这一比例，513名女性，487男性，这就是分层采样，人口划分为均匀的子集，每个子集称为一层，然后从每层中抽取正确的实例数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing[\"median_income\"].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     8.3252\n",
       "1     8.3014\n",
       "2     7.2574\n",
       "3     5.6431\n",
       "4     3.8462\n",
       "5     4.0368\n",
       "6     3.6591\n",
       "7     3.1200\n",
       "8     2.0804\n",
       "9     3.6912\n",
       "10    3.2031\n",
       "11    3.2705\n",
       "12    3.0750\n",
       "13    2.6736\n",
       "14    1.9167\n",
       "15    2.1250\n",
       "16    2.7750\n",
       "17    2.1202\n",
       "18    1.9911\n",
       "19    2.6033\n",
       "Name: median_income, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"median_income\"].head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 改造数据，希望测试集合数据能够代表整个数据集中不同类型的收入\n",
    "* 创建收入类表属性\n",
    "* 分层数不应该太多，但是每个层级数据量要够多\n",
    "* 将收入中位数除以1.5，减少收入类别的数量，使用ceil取整数，得到离散的类表种类，将大于5的合并为5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.1250     49\n",
       "15.0001    49\n",
       "2.8750     46\n",
       "4.1250     44\n",
       "2.6250     44\n",
       "           ..\n",
       "4.1514      1\n",
       "1.2614      1\n",
       "2.0294      1\n",
       "6.7079      1\n",
       "3.7306      1\n",
       "Name: median_income, Length: 12928, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"median_income\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing[\"income_category\"]=np.ceil(housing[\"median_income\"]/1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0     7236\n",
       "2.0     6581\n",
       "4.0     3639\n",
       "5.0     1423\n",
       "1.0      822\n",
       "6.0      532\n",
       "7.0      189\n",
       "8.0      105\n",
       "9.0       50\n",
       "11.0      49\n",
       "10.0      14\n",
       "Name: income_category, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"income_category\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     6.0\n",
       "1     6.0\n",
       "2     5.0\n",
       "3     4.0\n",
       "4     3.0\n",
       "5     3.0\n",
       "6     3.0\n",
       "7     3.0\n",
       "8     2.0\n",
       "9     3.0\n",
       "10    3.0\n",
       "11    3.0\n",
       "12    3.0\n",
       "13    2.0\n",
       "14    2.0\n",
       "15    2.0\n",
       "16    2.0\n",
       "17    2.0\n",
       "18    2.0\n",
       "19    2.0\n",
       "Name: income_category, dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['income_category'].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing['income_category'].where(housing[\"income_category\"]<5,5.0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5.0\n",
       "1     5.0\n",
       "2     5.0\n",
       "3     4.0\n",
       "4     3.0\n",
       "5     3.0\n",
       "6     3.0\n",
       "7     3.0\n",
       "8     2.0\n",
       "9     3.0\n",
       "10    3.0\n",
       "11    3.0\n",
       "12    3.0\n",
       "13    2.0\n",
       "14    2.0\n",
       "15    2.0\n",
       "16    2.0\n",
       "17    2.0\n",
       "18    2.0\n",
       "19    2.0\n",
       "Name: income_category, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['income_category'].head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pd.cut()的作用，有点类似给成绩设定优良中差，比如：0-59分为差，60-70分为中，71-80分为优秀等等\n",
    " 把连续值转换成类别标签 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.random.seed(666)\n",
    "\n",
    "# score_list = np.random.randint(25, 100, size=20)\n",
    "# print(score_list)\n",
    "# # [27 70 55 87 95 98 55 61 86 76 85 53 39 88 41 71 64 94 38 94]\n",
    "\n",
    "# #　指定多个区间\n",
    "# bins = [0, 59, 70, 80, 100]\n",
    "\n",
    "# score_cut = pd.cut(score_list, bins)\n",
    "# print(type(score_cut)) # <class 'pandas.core.arrays.categorical.Categorical'>\n",
    "# print(score_cut)\n",
    "# '''\n",
    "# [(0, 59], (59, 70], (0, 59], (80, 100], (80, 100], ..., (70, 80], (59, 70], (80, 100], (0, 59], (80, 100]]\n",
    "# Length: 20\n",
    "# Categories (4, interval[int64]): [(0, 59] < (59, 70] < (70, 80] < (80, 100]]\n",
    "# '''\n",
    "# print(pd.value_counts(score_cut)) # 统计每个区间人数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0    7236\n",
       "2.0    6581\n",
       "4.0    3639\n",
       "5.0    2362\n",
       "1.0     822\n",
       "Name: income_category, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"income_category\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#np.inf表示正大无穷\n",
    "#bins 指定区间\n",
    "housing[\"income_category\"]=pd.cut(housing[\"median_income\"],bins=[0.,1.5,3.0,4.5,6.,np.inf],labels=[1,2,3,4,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(housing[\"median_income\"])#housing[\"median_income\"]表示取出来的值是median_income列值，是pandas的Series类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    7236\n",
       "2    6581\n",
       "4    3639\n",
       "5    2362\n",
       "1     822\n",
       "Name: income_category, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"income_category\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing[\"income_category\"].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 根据收入类别进行分层采样\n",
    "\n",
    "\n",
    "交叉验证(Cross-validation)\n",
    "交叉验证是指在给定的建模样本中，拿出其中的大部分样本进行模型训练，生成模型，留小部分样本用刚建立的模型进行预测，\n",
    "求这小部分样本的预测误差，记录它们的平方加和。这个过程一直进行，直到所有的样本都被预测了一次而且仅被预测一次，\n",
    "比较每组的预测误差，选取误差最小的那一组作为训练模型\n",
    "\n",
    "\n",
    "# 参数 n_splits是将训练数据分成train/test对的组数，可根据需要进行设置，默认为10\n",
    " 参数test_size和train_size是用来设置train/test对中train和test所占的比例。例如：\n",
    "1.提供10个数据num进行训练和测试集划分\n",
    "2.设置train_size=0.8 test_size=0.2\n",
    "3.train_num=num*train_size=8 test_num=num*test_size=2\n",
    "4.即10个数据，进行划分以后8个是训练数据，2个是测试数据\n",
    "\n",
    "\n",
    "# 函数作用描述\n",
    "1.其产生指定数量的独立的train/test数据集划分数据集划分成n组。\n",
    "2.首先将样本随机打乱，然后根据设置参数划分出train/test对。\n",
    "3.其创建的每一组划分将保证每组类比比例相同。即第一组训练数据类别比例为2:1，则后面每组类别都满足这个比例\n",
    "    \n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.model_selection import StratifiedShuffleSplit\n",
    "# import numpy as np\n",
    "# X = np.array([[1, 2], [3, 4], [1, 2], [3, 4],\n",
    "#               [1, 2],[3, 4], [1, 2], [3, 4]])#训练数据集8*2\n",
    "# y = np.array([0, 0, 1, 1,0,0,1,1])#类别数据集8*1\n",
    "\n",
    "# ss=StratifiedShuffleSplit(n_splits=5,test_size=0.25,train_size=0.75,random_state=0)#分成5组，测试比例为0.25，训练比例是0.75\n",
    "\n",
    "# for train_index, test_index in ss.split(X, y):\n",
    "#    print(\"TRAIN:\", train_index, \"TEST:\", test_index)#获得索引值\n",
    "#    X_train, X_test = X[train_index], X[test_index]#训练集对应的值\n",
    "#    y_train, y_test = y[train_index], y[test_index]#类别集对应的值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "split=StratifiedShuffleSplit(n_splits=1,test_size=0.2,random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        5\n",
       "1        5\n",
       "2        5\n",
       "3        4\n",
       "4        3\n",
       "        ..\n",
       "20635    2\n",
       "20636    2\n",
       "20637    2\n",
       "20638    2\n",
       "20639    2\n",
       "Name: income_category, Length: 20640, dtype: category\n",
       "Categories (5, int64): [1 < 2 < 3 < 4 < 5]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"income_category\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# df定义了一个index，那么loc就根据这个index来索引对应的行。iloc并不是根据index来索引，而是根据行号来索引，行号从0开始，逐次加1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "for train_index,test_index in split.split(housing,housing[\"income_category\"]):\n",
    "    strat_train_set = housing.loc[train_index]\n",
    "    strat_test_set = housing.loc[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16512, 11)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_train_set.shape\n",
    "# strat_test_set.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>17606</td>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>286600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18632</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>340600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14650</td>\n",
       "      <td>-117.20</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>196900.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3230</td>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>46300.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3555</td>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>254500.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19480</td>\n",
       "      <td>-120.97</td>\n",
       "      <td>37.66</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2930.0</td>\n",
       "      <td>588.0</td>\n",
       "      <td>1448.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>3.5395</td>\n",
       "      <td>127900.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8879</td>\n",
       "      <td>-118.50</td>\n",
       "      <td>34.04</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2233.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>769.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>8.3839</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13685</td>\n",
       "      <td>-117.24</td>\n",
       "      <td>34.15</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2041.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>140200.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4937</td>\n",
       "      <td>-118.26</td>\n",
       "      <td>33.99</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1865.0</td>\n",
       "      <td>465.0</td>\n",
       "      <td>1916.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>1.8242</td>\n",
       "      <td>95000.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4861</td>\n",
       "      <td>-118.28</td>\n",
       "      <td>34.02</td>\n",
       "      <td>29.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>2690.0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>0.4999</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
       "18632    -121.93     37.05                14.0        679.0           108.0   \n",
       "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
       "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
       "19480    -120.97     37.66                24.0       2930.0           588.0   \n",
       "8879     -118.50     34.04                52.0       2233.0           317.0   \n",
       "13685    -117.24     34.15                26.0       2041.0           293.0   \n",
       "4937     -118.26     33.99                47.0       1865.0           465.0   \n",
       "4861     -118.28     34.02                29.0        515.0           229.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "17606       710.0       339.0         2.7042            286600.0   \n",
       "18632       306.0       113.0         6.4214            340600.0   \n",
       "14650       936.0       462.0         2.8621            196900.0   \n",
       "3230       1460.0       353.0         1.8839             46300.0   \n",
       "3555       4459.0      1463.0         3.0347            254500.0   \n",
       "19480      1448.0       570.0         3.5395            127900.0   \n",
       "8879        769.0       277.0         8.3839            500001.0   \n",
       "13685       936.0       375.0         6.0000            140200.0   \n",
       "4937       1916.0       438.0         1.8242             95000.0   \n",
       "4861       2690.0       217.0         0.4999            500001.0   \n",
       "\n",
       "      ocean_proximity income_category  \n",
       "17606       <1H OCEAN               2  \n",
       "18632       <1H OCEAN               5  \n",
       "14650      NEAR OCEAN               2  \n",
       "3230           INLAND               2  \n",
       "3555        <1H OCEAN               3  \n",
       "19480          INLAND               3  \n",
       "8879        <1H OCEAN               5  \n",
       "13685          INLAND               4  \n",
       "4937        <1H OCEAN               2  \n",
       "4861        <1H OCEAN               1  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_train_set.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 查看所有住房数据根据收入类别比例分布，收入占类别百分比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    0.350533\n",
       "2    0.318798\n",
       "4    0.176357\n",
       "5    0.114583\n",
       "1    0.039729\n",
       "Name: income_category, dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_test_set['income_category'].value_counts()/len(strat_test_set)  # 分层抽样测试集合，各值占百分比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    1447\n",
       "2    1316\n",
       "4     728\n",
       "5     473\n",
       "1     164\n",
       "Name: income_category, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_test_set['income_category'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "def income_cat_proportions(data):\n",
    "    return data[\"income_category\"].value_counts()/len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_props=pd.DataFrame({\n",
    "    \"全部数据\": income_cat_proportions(housing),\n",
    "    \"分层抽样\": income_cat_proportions(strat_test_set),\n",
    "    \"随机抽样\": income_cat_proportions(test_set),\n",
    "}).sort_index()\n",
    "compare_props[\"随机. %error\"] = 100 * compare_props[\"随机抽样\"] / compare_props[\"全部数据\"] - 100\n",
    "compare_props[\"分层. %error\"] = 100 * compare_props[\"分层抽样\"] / compare_props[\"全部数据\"] - 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>全部数据</th>\n",
       "      <th>分层抽样</th>\n",
       "      <th>随机抽样</th>\n",
       "      <th>随机. %error</th>\n",
       "      <th>分层. %error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.039826</td>\n",
       "      <td>0.039729</td>\n",
       "      <td>0.040213</td>\n",
       "      <td>0.973236</td>\n",
       "      <td>-0.243309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.318847</td>\n",
       "      <td>0.318798</td>\n",
       "      <td>0.324370</td>\n",
       "      <td>1.732260</td>\n",
       "      <td>-0.015195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.350581</td>\n",
       "      <td>0.350533</td>\n",
       "      <td>0.358527</td>\n",
       "      <td>2.266446</td>\n",
       "      <td>-0.013820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.176308</td>\n",
       "      <td>0.176357</td>\n",
       "      <td>0.167393</td>\n",
       "      <td>-5.056334</td>\n",
       "      <td>0.027480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.114438</td>\n",
       "      <td>0.114583</td>\n",
       "      <td>0.109496</td>\n",
       "      <td>-4.318374</td>\n",
       "      <td>0.127011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       全部数据      分层抽样      随机抽样  随机. %error  分层. %error\n",
       "1  0.039826  0.039729  0.040213    0.973236   -0.243309\n",
       "2  0.318847  0.318798  0.324370    1.732260   -0.015195\n",
       "3  0.350581  0.350533  0.358527    2.266446   -0.013820\n",
       "4  0.176308  0.176357  0.167393   -5.056334    0.027480\n",
       "5  0.114438  0.114583  0.109496   -4.318374    0.127011"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_props"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 把测试集已经处理完成，用分层抽样的训练集数据来进行分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 将地理数据可视化\n",
    "建立一个各区域的分布图，以便于数据可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='longitude', ylabel='latitude'>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制点图\n",
    "housing.plot(kind='scatter', x='longitude', y = 'latitude',alpha=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "e:\\python3.7\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\tools.py:307: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "e:\\python3.7\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\tools.py:307: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "e:\\python3.7\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\tools.py:313: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n",
      "e:\\python3.7\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\tools.py:313: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='longitude', ylabel='latitude'>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 我们的大脑非常善于从图片中发现模式，但是需要玩转可视化参数才能让这些模式凸显出来\n",
    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.4,\n",
    "    s=housing[\"population\"]/100, label=\"population\", figsize=(10,7),\n",
    "    c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"), colorbar=True,\n",
    "    sharex=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: './california.png'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-81-e504b7347a93>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimage\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mmpimg\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mcalifornia_img\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmpimg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'./california.png'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.4,\n\u001b[0;32m      4\u001b[0m     \u001b[0ms\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mhousing\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"population\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"population\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m7\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"median_house_value\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_cmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"jet\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolorbar\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32me:\\python3.7\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36mimread\u001b[1;34m(fname, format)\u001b[0m\n\u001b[0;32m   1474\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0murllib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0murlopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1475\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mimread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1476\u001b[1;33m     \u001b[1;32mwith\u001b[0m \u001b[0mimg_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mimage\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1477\u001b[0m         return (_pil_png_to_float_array(image)\n\u001b[0;32m   1478\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mPIL\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPngImagePlugin\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPngImageFile\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32me:\\python3.7\\lib\\site-packages\\PIL\\ImageFile.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, fp, filename)\u001b[0m\n\u001b[0;32m    103\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misPath\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    104\u001b[0m             \u001b[1;31m# filename\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 105\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"rb\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    106\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilename\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    107\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_exclusive_fp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './california.png'"
     ]
    }
   ],
   "source": [
    "import matplotlib.image as mpimg\n",
    "california_img = mpimg.imread('./california.png')\n",
    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.4,\n",
    "    s=housing[\"population\"]/100, label=\"population\", figsize=(10,7),\n",
    "    c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"), colorbar=False,\n",
    "    sharex=False)\n",
    "plt.imshow(california_img, extent=[-124.55, -113.80, 32.45, 42.05], alpha=0.5,\n",
    "           cmap=plt.get_cmap(\"jet\"))\n",
    "plt.ylabel(\"Latitude\", fontsize=14)\n",
    "plt.xlabel(\"Longitude\", fontsize=14)\n",
    "\n",
    "prices = housing[\"median_house_value\"]\n",
    "tick_values = np.linspace(prices.min(), prices.max(), 11)#十一等分\n",
    "cbar = plt.colorbar()\n",
    "cbar.ax.set_yticklabels([\"$%dk\"%(round(v/1000)) for v in tick_values], fontsize=14)\n",
    "cbar.set_label('Median House Value', fontsize=16)\n",
    "plt.legend(fontsize=16)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 寻找特征相关性"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## corr() 计算出每对特征之间的相关系数， 称为皮尔逊相关系数\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>longitude</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.924664</td>\n",
       "      <td>-0.108197</td>\n",
       "      <td>0.044568</td>\n",
       "      <td>0.069608</td>\n",
       "      <td>0.099773</td>\n",
       "      <td>0.055310</td>\n",
       "      <td>-0.015176</td>\n",
       "      <td>-0.045967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>latitude</td>\n",
       "      <td>-0.924664</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011173</td>\n",
       "      <td>-0.036100</td>\n",
       "      <td>-0.066983</td>\n",
       "      <td>-0.108785</td>\n",
       "      <td>-0.071035</td>\n",
       "      <td>-0.079809</td>\n",
       "      <td>-0.144160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>housing_median_age</td>\n",
       "      <td>-0.108197</td>\n",
       "      <td>0.011173</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.361262</td>\n",
       "      <td>-0.320451</td>\n",
       "      <td>-0.296244</td>\n",
       "      <td>-0.302916</td>\n",
       "      <td>-0.119034</td>\n",
       "      <td>0.105623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>total_rooms</td>\n",
       "      <td>0.044568</td>\n",
       "      <td>-0.036100</td>\n",
       "      <td>-0.361262</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.930380</td>\n",
       "      <td>0.857126</td>\n",
       "      <td>0.918484</td>\n",
       "      <td>0.198050</td>\n",
       "      <td>0.134153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>total_bedrooms</td>\n",
       "      <td>0.069608</td>\n",
       "      <td>-0.066983</td>\n",
       "      <td>-0.320451</td>\n",
       "      <td>0.930380</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.877747</td>\n",
       "      <td>0.979728</td>\n",
       "      <td>-0.007723</td>\n",
       "      <td>0.049686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>population</td>\n",
       "      <td>0.099773</td>\n",
       "      <td>-0.108785</td>\n",
       "      <td>-0.296244</td>\n",
       "      <td>0.857126</td>\n",
       "      <td>0.877747</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.907222</td>\n",
       "      <td>0.004834</td>\n",
       "      <td>-0.024650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>households</td>\n",
       "      <td>0.055310</td>\n",
       "      <td>-0.071035</td>\n",
       "      <td>-0.302916</td>\n",
       "      <td>0.918484</td>\n",
       "      <td>0.979728</td>\n",
       "      <td>0.907222</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013033</td>\n",
       "      <td>0.065843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>median_income</td>\n",
       "      <td>-0.015176</td>\n",
       "      <td>-0.079809</td>\n",
       "      <td>-0.119034</td>\n",
       "      <td>0.198050</td>\n",
       "      <td>-0.007723</td>\n",
       "      <td>0.004834</td>\n",
       "      <td>0.013033</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.688075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>median_house_value</td>\n",
       "      <td>-0.045967</td>\n",
       "      <td>-0.144160</td>\n",
       "      <td>0.105623</td>\n",
       "      <td>0.134153</td>\n",
       "      <td>0.049686</td>\n",
       "      <td>-0.024650</td>\n",
       "      <td>0.065843</td>\n",
       "      <td>0.688075</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    longitude  latitude  housing_median_age  total_rooms  \\\n",
       "longitude            1.000000 -0.924664           -0.108197     0.044568   \n",
       "latitude            -0.924664  1.000000            0.011173    -0.036100   \n",
       "housing_median_age  -0.108197  0.011173            1.000000    -0.361262   \n",
       "total_rooms          0.044568 -0.036100           -0.361262     1.000000   \n",
       "total_bedrooms       0.069608 -0.066983           -0.320451     0.930380   \n",
       "population           0.099773 -0.108785           -0.296244     0.857126   \n",
       "households           0.055310 -0.071035           -0.302916     0.918484   \n",
       "median_income       -0.015176 -0.079809           -0.119034     0.198050   \n",
       "median_house_value  -0.045967 -0.144160            0.105623     0.134153   \n",
       "\n",
       "                    total_bedrooms  population  households  median_income  \\\n",
       "longitude                 0.069608    0.099773    0.055310      -0.015176   \n",
       "latitude                 -0.066983   -0.108785   -0.071035      -0.079809   \n",
       "housing_median_age       -0.320451   -0.296244   -0.302916      -0.119034   \n",
       "total_rooms               0.930380    0.857126    0.918484       0.198050   \n",
       "total_bedrooms            1.000000    0.877747    0.979728      -0.007723   \n",
       "population                0.877747    1.000000    0.907222       0.004834   \n",
       "households                0.979728    0.907222    1.000000       0.013033   \n",
       "median_income            -0.007723    0.004834    0.013033       1.000000   \n",
       "median_house_value        0.049686   -0.024650    0.065843       0.688075   \n",
       "\n",
       "                    median_house_value  \n",
       "longitude                    -0.045967  \n",
       "latitude                     -0.144160  \n",
       "housing_median_age            0.105623  \n",
       "total_rooms                   0.134153  \n",
       "total_bedrooms                0.049686  \n",
       "population                   -0.024650  \n",
       "households                    0.065843  \n",
       "median_income                 0.688075  \n",
       "median_house_value            1.000000  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix =  housing.corr()\n",
    "corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value    1.000000\n",
       "median_income         0.688075\n",
       "total_rooms           0.134153\n",
       "housing_median_age    0.105623\n",
       "households            0.065843\n",
       "total_bedrooms        0.049686\n",
       "population           -0.024650\n",
       "longitude            -0.045967\n",
       "latitude             -0.144160\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 值的相关性解释\n",
    "1，相关系数范围 从 -1 变化到 1，越接近1 ，表示越强的正相关，，比如，当收入中位数上升时，房价中位数也趋于上升，\n",
    "2，当系数接近-1 ，则表示有强烈的负相关，纬度和房价中位数之间出现轻微的负相关，越往北走，房价倾向于下降，\n",
    "3，系数靠近0 ，说明二者之间没有线性相关性，\n",
    "4，相关系数仅检测 线性相关性 ，如果x上升，y上升或者下降，可能会彻底遗漏非线性相关性，例如 如果x接近于0，y上升"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用padans scatter_matrix 函数，可以绘制特征之间的相关性\n",
    "from pandas.plotting import scatter_matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
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       "      <td>7.2574</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20635</td>\n",
       "      <td>78100.0</td>\n",
       "      <td>1.5603</td>\n",
       "      <td>1665.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20636</td>\n",
       "      <td>77100.0</td>\n",
       "      <td>2.5568</td>\n",
       "      <td>697.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20637</td>\n",
       "      <td>92300.0</td>\n",
       "      <td>1.7000</td>\n",
       "      <td>2254.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20638</td>\n",
       "      <td>84700.0</td>\n",
       "      <td>1.8672</td>\n",
       "      <td>1860.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20639</td>\n",
       "      <td>89400.0</td>\n",
       "      <td>2.3886</td>\n",
       "      <td>2785.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20640 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       median_house_value  median_income  total_rooms  housing_median_age\n",
       "0                452600.0         8.3252        880.0                41.0\n",
       "1                358500.0         8.3014       7099.0                21.0\n",
       "2                352100.0         7.2574       1467.0                52.0\n",
       "3                341300.0         5.6431       1274.0                52.0\n",
       "4                342200.0         3.8462       1627.0                52.0\n",
       "...                   ...            ...          ...                 ...\n",
       "20635             78100.0         1.5603       1665.0                25.0\n",
       "20636             77100.0         2.5568        697.0                18.0\n",
       "20637             92300.0         1.7000       2254.0                17.0\n",
       "20638             84700.0         1.8672       1860.0                18.0\n",
       "20639             89400.0         2.3886       2785.0                16.0\n",
       "\n",
       "[20640 rows x 4 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "attributes = ['median_house_value', 'median_income', 'total_rooms', 'housing_median_age']\n",
    "housing[attributes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:xlabel='median_house_value', ylabel='median_house_value'>,\n",
       "        <AxesSubplot:xlabel='median_income', ylabel='median_house_value'>,\n",
       "        <AxesSubplot:xlabel='total_rooms', ylabel='median_house_value'>,\n",
       "        <AxesSubplot:xlabel='housing_median_age', ylabel='median_house_value'>],\n",
       "       [<AxesSubplot:xlabel='median_house_value', ylabel='median_income'>,\n",
       "        <AxesSubplot:xlabel='median_income', ylabel='median_income'>,\n",
       "        <AxesSubplot:xlabel='total_rooms', ylabel='median_income'>,\n",
       "        <AxesSubplot:xlabel='housing_median_age', ylabel='median_income'>],\n",
       "       [<AxesSubplot:xlabel='median_house_value', ylabel='total_rooms'>,\n",
       "        <AxesSubplot:xlabel='median_income', ylabel='total_rooms'>,\n",
       "        <AxesSubplot:xlabel='total_rooms', ylabel='total_rooms'>,\n",
       "        <AxesSubplot:xlabel='housing_median_age', ylabel='total_rooms'>],\n",
       "       [<AxesSubplot:xlabel='median_house_value', ylabel='housing_median_age'>,\n",
       "        <AxesSubplot:xlabel='median_income', ylabel='housing_median_age'>,\n",
       "        <AxesSubplot:xlabel='total_rooms', ylabel='housing_median_age'>,\n",
       "        <AxesSubplot:xlabel='housing_median_age', ylabel='housing_median_age'>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "type(housing[attributes])\n",
    "scatter_matrix(housing[attributes], figsize = (12, 8) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "#绘制median_income与median_house_value之间的线性关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 16.0, 0.0, 550000.0)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind=\"scatter\",x=\"median_income\",y=\"median_house_value\",alpha=0.1)\n",
    "plt.axis([0,16,0,550000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 50万美元是一条清晰的线，被设置上限了， 45w，35w，28w，也有虚线， 可能是异常值，我可以在后期把数据删除掉"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 在给机器学习算法输入数据之前，我们识别出了一些异常数据，需要提前清理掉，也发现了不同属性之间的某些联系，跟目标属性相关的联系\n",
    "2. 某些属性分布显示出了明显的“重尾”分布，对进行转换处理，计算其对数\n",
    "3. 在给机器学习算法输入数据之前，最后一件事儿，尝试各种属性的组合\n",
    "4. 每个项目历程都不一样，大致思路都相识"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实验不同特征之间的组合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing['rooms_per_household'] = housing['total_rooms']/housing['households']\n",
    "housing['bedrooms_per_room'] = housing['total_bedrooms']/housing['total_rooms']\n",
    "housing['population_per_household'] = housing['population']/housing['households']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value          1.000000\n",
       "median_income               0.688075\n",
       "rooms_per_household         0.151948\n",
       "total_rooms                 0.134153\n",
       "housing_median_age          0.105623\n",
       "households                  0.065843\n",
       "total_bedrooms              0.049686\n",
       "population_per_household   -0.023737\n",
       "population                 -0.024650\n",
       "longitude                  -0.045967\n",
       "latitude                   -0.144160\n",
       "bedrooms_per_room          -0.255880\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 关联矩阵\n",
    "corr_matrix = housing.corr()\n",
    "corr_matrix['median_house_value'].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 机器学习算法的数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>17606</td>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>286600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18632</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>340600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14650</td>\n",
       "      <td>-117.20</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>196900.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3230</td>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>46300.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3555</td>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>254500.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
       "18632    -121.93     37.05                14.0        679.0           108.0   \n",
       "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
       "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "17606       710.0       339.0         2.7042            286600.0   \n",
       "18632       306.0       113.0         6.4214            340600.0   \n",
       "14650       936.0       462.0         2.8621            196900.0   \n",
       "3230       1460.0       353.0         1.8839             46300.0   \n",
       "3555       4459.0      1463.0         3.0347            254500.0   \n",
       "\n",
       "      ocean_proximity income_category  \n",
       "17606       <1H OCEAN               2  \n",
       "18632       <1H OCEAN               5  \n",
       "14650      NEAR OCEAN               2  \n",
       "3230           INLAND               2  \n",
       "3555        <1H OCEAN               3  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_train_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_labels=strat_train_set[\"median_house_value\"].copy()#将数据集的数据与标签分开"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing=strat_train_set.drop(\"median_house_value\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>17606</td>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18632</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14650</td>\n",
       "      <td>-117.20</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3230</td>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3555</td>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
       "18632    -121.93     37.05                14.0        679.0           108.0   \n",
       "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
       "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
       "\n",
       "       population  households  median_income ocean_proximity income_category  \n",
       "17606       710.0       339.0         2.7042       <1H OCEAN               2  \n",
       "18632       306.0       113.0         6.4214       <1H OCEAN               5  \n",
       "14650       936.0       462.0         2.8621      NEAR OCEAN               2  \n",
       "3230       1460.0       353.0         1.8839          INLAND               2  \n",
       "3555       4459.0      1463.0         3.0347       <1H OCEAN               3  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16512 entries, 17606 to 15775\n",
      "Data columns (total 10 columns):\n",
      "longitude             16512 non-null float64\n",
      "latitude              16512 non-null float64\n",
      "housing_median_age    16512 non-null float64\n",
      "total_rooms           16512 non-null float64\n",
      "total_bedrooms        16354 non-null float64\n",
      "population            16512 non-null float64\n",
      "households            16512 non-null float64\n",
      "median_income         16512 non-null float64\n",
      "ocean_proximity       16512 non-null object\n",
      "income_category       16512 non-null category\n",
      "dtypes: category(1), float64(8), object(1)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据清洗"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 大多数机器学算法无法在缺失的特征上工作，创建一些函数辅助，total_bedrooms有缺失，\n",
    "2. 放弃这些区域\n",
    "3. 放弃这个属性\n",
    "4. 将缺失值 设置为 某个值，（0， 平均数或者中位数都可以）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>4629</td>\n",
       "      <td>-118.30</td>\n",
       "      <td>34.07</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3759.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3296.0</td>\n",
       "      <td>1462.0</td>\n",
       "      <td>2.2708</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6068</td>\n",
       "      <td>-117.86</td>\n",
       "      <td>34.01</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4632.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3038.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>5.1762</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17923</td>\n",
       "      <td>-121.97</td>\n",
       "      <td>37.35</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1955.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>999.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>4.6328</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13656</td>\n",
       "      <td>-117.30</td>\n",
       "      <td>34.05</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2155.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>1.6675</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19252</td>\n",
       "      <td>-122.79</td>\n",
       "      <td>38.48</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6837.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3468.0</td>\n",
       "      <td>1405.0</td>\n",
       "      <td>3.1662</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "4629     -118.30     34.07                18.0       3759.0             NaN   \n",
       "6068     -117.86     34.01                16.0       4632.0             NaN   \n",
       "17923    -121.97     37.35                30.0       1955.0             NaN   \n",
       "13656    -117.30     34.05                 6.0       2155.0             NaN   \n",
       "19252    -122.79     38.48                 7.0       6837.0             NaN   \n",
       "\n",
       "       population  households  median_income ocean_proximity income_category  \n",
       "4629       3296.0      1462.0         2.2708       <1H OCEAN               2  \n",
       "6068       3038.0       727.0         5.1762       <1H OCEAN               4  \n",
       "17923       999.0       386.0         4.6328       <1H OCEAN               4  \n",
       "13656      1039.0       391.0         1.6675          INLAND               2  \n",
       "19252      3468.0      1405.0         3.1662       <1H OCEAN               3  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows=housing[housing.isnull().any(axis=1)]\n",
    "rows.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 放弃这些区域\n",
    "## rows.dropna(subset=['total_bedrooms'])\n",
    "# 放弃这个属性\n",
    "## rows.drop(\"total_bedrooms\",axis=1)\n",
    "# 将缺失的值设置为某个值，（0，平均数或者中位数都可以）\n",
    "## 创建一个imputer实例，指定你要用的属性的中位数替换该属性的缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleImputer(strategy='median')"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer=SimpleImputer(strategy=\"median\")\n",
    "housing_num=housing.drop(\"ocean_proximity\",axis=1)\n",
    "imputer.fit(housing_num)#fit()方法将imputer实例适配到训练集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-118.51  ,   34.26  ,   29.    , 2119.5   ,  433.    , 1164.    ,\n",
       "        408.    ,    3.5409,    3.    ])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-118.51  ,   34.26  ,   29.    , 2119.5   ,  433.    , 1164.    ,\n",
       "        408.    ,    3.5409,    3.    ])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num.median().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-121.89  ,   37.29  ,   38.    , ...,  339.    ,    2.7042,\n",
       "           2.    ],\n",
       "       [-121.93  ,   37.05  ,   14.    , ...,  113.    ,    6.4214,\n",
       "           5.    ],\n",
       "       [-117.2   ,   32.77  ,   31.    , ...,  462.    ,    2.8621,\n",
       "           2.    ],\n",
       "       ...,\n",
       "       [-116.4   ,   34.09  ,    9.    , ...,  765.    ,    3.2723,\n",
       "           3.    ],\n",
       "       [-118.01  ,   33.82  ,   31.    , ...,  356.    ,    4.0625,\n",
       "           3.    ],\n",
       "       [-122.45  ,   37.77  ,   52.    , ...,  639.    ,    3.575 ,\n",
       "           3.    ]])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=imputer.transform(housing_num)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
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       "      <td>588.0</td>\n",
       "      <td>1448.0</td>\n",
       "      <td>570.0</td>\n",
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       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <td>8879</td>\n",
       "      <td>-118.50</td>\n",
       "      <td>34.04</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2233.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>769.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>8.3839</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <td>13685</td>\n",
       "      <td>-117.24</td>\n",
       "      <td>34.15</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2041.0</td>\n",
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       "      <td>0.4999</td>\n",
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       "      <td>38.02</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4157.0</td>\n",
       "      <td>951.0</td>\n",
       "      <td>2734.0</td>\n",
       "      <td>879.0</td>\n",
       "      <td>2.7981</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19684</td>\n",
       "      <td>-121.62</td>\n",
       "      <td>39.14</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2183.0</td>\n",
       "      <td>559.0</td>\n",
       "      <td>1202.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>1.6902</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19234</td>\n",
       "      <td>-122.69</td>\n",
       "      <td>38.51</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3364.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>1442.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>6.6854</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13956</td>\n",
       "      <td>-117.06</td>\n",
       "      <td>34.17</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2520.0</td>\n",
       "      <td>582.0</td>\n",
       "      <td>416.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>2.7120</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2390</td>\n",
       "      <td>-119.46</td>\n",
       "      <td>36.91</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>495.0</td>\n",
       "      <td>1184.0</td>\n",
       "      <td>429.0</td>\n",
       "      <td>3.9141</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11176</td>\n",
       "      <td>-117.96</td>\n",
       "      <td>33.83</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2838.0</td>\n",
       "      <td>649.0</td>\n",
       "      <td>1758.0</td>\n",
       "      <td>593.0</td>\n",
       "      <td>3.3831</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15614</td>\n",
       "      <td>-122.41</td>\n",
       "      <td>37.81</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1178.0</td>\n",
       "      <td>545.0</td>\n",
       "      <td>592.0</td>\n",
       "      <td>441.0</td>\n",
       "      <td>3.6728</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2953</td>\n",
       "      <td>-119.02</td>\n",
       "      <td>35.35</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1239.0</td>\n",
       "      <td>251.0</td>\n",
       "      <td>776.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>1.9830</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13209</td>\n",
       "      <td>-117.72</td>\n",
       "      <td>34.05</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1841.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>1243.0</td>\n",
       "      <td>394.0</td>\n",
       "      <td>4.0614</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6569</td>\n",
       "      <td>-118.15</td>\n",
       "      <td>34.20</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1505.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>857.0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>4.5000</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
       "18632    -121.93     37.05                14.0        679.0           108.0   \n",
       "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
       "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
       "19480    -120.97     37.66                24.0       2930.0           588.0   \n",
       "8879     -118.50     34.04                52.0       2233.0           317.0   \n",
       "13685    -117.24     34.15                26.0       2041.0           293.0   \n",
       "4937     -118.26     33.99                47.0       1865.0           465.0   \n",
       "4861     -118.28     34.02                29.0        515.0           229.0   \n",
       "16365    -121.31     38.02                24.0       4157.0           951.0   \n",
       "19684    -121.62     39.14                41.0       2183.0           559.0   \n",
       "19234    -122.69     38.51                18.0       3364.0           501.0   \n",
       "13956    -117.06     34.17                21.0       2520.0           582.0   \n",
       "2390     -119.46     36.91                12.0       2980.0           495.0   \n",
       "11176    -117.96     33.83                30.0       2838.0           649.0   \n",
       "15614    -122.41     37.81                25.0       1178.0           545.0   \n",
       "2953     -119.02     35.35                42.0       1239.0           251.0   \n",
       "13209    -117.72     34.05                 8.0       1841.0           409.0   \n",
       "6569     -118.15     34.20                46.0       1505.0           261.0   \n",
       "\n",
       "       population  households  median_income  income_category  \n",
       "17606       710.0       339.0         2.7042              2.0  \n",
       "18632       306.0       113.0         6.4214              5.0  \n",
       "14650       936.0       462.0         2.8621              2.0  \n",
       "3230       1460.0       353.0         1.8839              2.0  \n",
       "3555       4459.0      1463.0         3.0347              3.0  \n",
       "19480      1448.0       570.0         3.5395              3.0  \n",
       "8879        769.0       277.0         8.3839              5.0  \n",
       "13685       936.0       375.0         6.0000              4.0  \n",
       "4937       1916.0       438.0         1.8242              2.0  \n",
       "4861       2690.0       217.0         0.4999              1.0  \n",
       "16365      2734.0       879.0         2.7981              2.0  \n",
       "19684      1202.0       506.0         1.6902              2.0  \n",
       "19234      1442.0       506.0         6.6854              5.0  \n",
       "13956       416.0       151.0         2.7120              2.0  \n",
       "2390       1184.0       429.0         3.9141              3.0  \n",
       "11176      1758.0       593.0         3.3831              3.0  \n",
       "15614       592.0       441.0         3.6728              3.0  \n",
       "2953        776.0       272.0         1.9830              2.0  \n",
       "13209      1243.0       394.0         4.0614              3.0  \n",
       "6569        857.0       269.0         4.5000              3.0  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr=pd.DataFrame(X,columns=housing_num.columns,index=housing_num.index)\n",
    "housing_tr.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16512 entries, 17606 to 15775\n",
      "Data columns (total 9 columns):\n",
      "longitude             16512 non-null float64\n",
      "latitude              16512 non-null float64\n",
      "housing_median_age    16512 non-null float64\n",
      "total_rooms           16512 non-null float64\n",
      "total_bedrooms        16512 non-null float64\n",
      "population            16512 non-null float64\n",
      "households            16512 non-null float64\n",
      "median_income         16512 non-null float64\n",
      "income_category       16512 non-null float64\n",
      "dtypes: float64(9)\n",
      "memory usage: 1.3 MB\n"
     ]
    }
   ],
   "source": [
    "housing_tr.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# scikit-Learn\n",
    " - 一致性\n",
    "     1.估算器 比如：各种机器算法 fit() 执行估算器\n",
    "     2.转换器\n",
    "         比如 LabelBinarizer transform() 执行转换数据集 fit_transform() 先估算，再转换\n",
    "     3.预测器 predict()对给定的新数据集进行预测score()评估测试集的预测质量\n",
    "- 检查\n",
    "    1.imputer.strategy_学习参数通过公共实例变量访问\n",
    "     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 处理文本和分类属性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>17606</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18632</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14650</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3230</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3555</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19480</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8879</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13685</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4937</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4861</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ocean_proximity\n",
       "17606       <1H OCEAN\n",
       "18632       <1H OCEAN\n",
       "14650      NEAR OCEAN\n",
       "3230           INLAND\n",
       "3555        <1H OCEAN\n",
       "19480          INLAND\n",
       "8879        <1H OCEAN\n",
       "13685          INLAND\n",
       "4937        <1H OCEAN\n",
       "4861        <1H OCEAN"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat=housing[['ocean_proximity']]\n",
    "housing_cat.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1H OCEAN     7276\n",
       "INLAND        5263\n",
       "NEAR OCEAN    2124\n",
       "NEAR BAY      1847\n",
       "ISLAND           2\n",
       "Name: ocean_proximity, dtype: int64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['ocean_proximity'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 将文本转化为对应的数字分类，使用转换器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 4, ..., 1, 0, 3])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder=LabelEncoder()\n",
    "housing_cat=housing['ocean_proximity']\n",
    "housing_cat_encoder=encoder.fit_transform(housing_cat)\n",
    "housing_cat_encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder.classes_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 机器学习算法会以为两个相近的数字比远的数字更相似，比如0，4 比 0，1相似度更高，\n",
    "2. 创建独热编码，当 <1H ,第0个属性为1，其余都为0， 列别是InLand时候，另一个属性为1，其余为0，1为热，0为冷，独热编码\n",
    "3. OneHotEncoder编码器, fit_trans需要二维数组， 转换housing_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "encoder=OneHotEncoder()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "housing_cat_encoder.reshape(-1,1)#数组变为矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_cat_hot=encoder.fit_transform(housing_cat_encoder.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<16512x5 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 16512 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 1.],\n",
       "       ...,\n",
       "       [0., 1., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 1., 0.]])"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_hot.toarray()#从稀释矩阵转换成numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一次完成两个转化 文本类型-整数类型-独热类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0, 0, 0],\n",
       "       [1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 1],\n",
       "       ...,\n",
       "       [0, 1, 0, 0, 0],\n",
       "       [1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 1, 0]])"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelBinarizer\n",
    "encoder=LabelBinarizer()\n",
    "housing_cat_1hot = encoder.fit_transform(housing_cat)\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 输出稀疏矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<16512x5 sparse matrix of type '<class 'numpy.int32'>'\n",
       "\twith 16512 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder = LabelBinarizer(sparse_output = True)\n",
    "housing_cat_1hot = encoder.fit_transform(housing_cat)\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 自定义转换器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 4, 5, 6)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "\n",
    "rooms_ix, bedrooms_ix, population_ix, household_ix = [list(housing.columns).index(col) for col in (\"total_rooms\", \"total_bedrooms\", \"population\", \"households\")]\n",
    "rooms_ix, bedrooms_ix, population_ix, household_ix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CombinedAttributesAdder(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self,add_bedrooms_per_room=True):\n",
    "        self.add_bedrooms_per_room=add_bedrooms_per_room\n",
    "    def fit(self,X,y=None):\n",
    "        return self\n",
    "    def transform(self,X,y=None):\n",
    "        rooms_per_household=X[:,rooms_ix]/X[:,household_ix]\n",
    "        population_per_household = X[:, population_ix] / X[:, household_ix]\n",
    "        if self.add_bedrooms_per_room:\n",
    "            bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]\n",
    "            return np.c_[X, rooms_per_household,population_per_household, bedrooms_per_room]\n",
    "        else:\n",
    "            return np.c_[X, rooms_per_household,population_per_household]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "attr_adder = CombinedAttributesAdder(add_bedrooms_per_room = False)\n",
    "housing_extra_attribs = attr_adder.transform(housing.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-121.89, 37.29, 38.0, ..., 2, 4.625368731563422,\n",
       "        2.094395280235988],\n",
       "       [-121.93, 37.05, 14.0, ..., 5, 6.008849557522124,\n",
       "        2.7079646017699117],\n",
       "       [-117.2, 32.77, 31.0, ..., 2, 4.225108225108225,\n",
       "        2.0259740259740258],\n",
       "       ...,\n",
       "       [-116.4, 34.09, 9.0, ..., 3, 6.34640522875817, 2.742483660130719],\n",
       "       [-118.01, 33.82, 31.0, ..., 3, 5.50561797752809,\n",
       "        3.808988764044944],\n",
       "       [-122.45, 37.77, 52.0, ..., 3, 4.843505477308295,\n",
       "        1.9859154929577465]], dtype=object)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_extra_attribs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>-121.89</td>\n",
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       "      <td>38</td>\n",
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       "      <td>710</td>\n",
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       "      <td>936</td>\n",
       "      <td>462</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>4.22511</td>\n",
       "      <td>2.02597</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25</td>\n",
       "      <td>1847</td>\n",
       "      <td>371</td>\n",
       "      <td>1460</td>\n",
       "      <td>353</td>\n",
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      ],
      "text/plain": [
       "        0      1   2     3     4     5     6       7           8  9       10  \\\n",
       "0 -121.89  37.29  38  1568   351   710   339  2.7042   <1H OCEAN  2  4.62537   \n",
       "1 -121.93  37.05  14   679   108   306   113  6.4214   <1H OCEAN  5  6.00885   \n",
       "2  -117.2  32.77  31  1952   471   936   462  2.8621  NEAR OCEAN  2  4.22511   \n",
       "3 -119.61  36.31  25  1847   371  1460   353  1.8839      INLAND  2  5.23229   \n",
       "4 -118.59  34.23  17  6592  1525  4459  1463  3.0347   <1H OCEAN  3  4.50581   \n",
       "\n",
       "        11  \n",
       "0   2.0944  \n",
       "1  2.70796  \n",
       "2  2.02597  \n",
       "3  4.13598  \n",
       "4  3.04785  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr=pd.DataFrame(housing_extra_attribs)\n",
    "housing_tr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
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       "      <td>3230</td>\n",
       "      <td>-119.61</td>\n",
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       "      <td>1460</td>\n",
       "      <td>353</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>INLAND</td>\n",
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       "      <td>5.23229</td>\n",
       "      <td>4.13598</td>\n",
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       "    <tr>\n",
       "      <td>3555</td>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17</td>\n",
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       "      <td>1525</td>\n",
       "      <td>4459</td>\n",
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       "      <td>3.0347</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>4.50581</td>\n",
       "      <td>3.04785</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      longitude latitude housing_median_age total_rooms total_bedrooms  \\\n",
       "17606   -121.89    37.29                 38        1568            351   \n",
       "18632   -121.93    37.05                 14         679            108   \n",
       "14650    -117.2    32.77                 31        1952            471   \n",
       "3230    -119.61    36.31                 25        1847            371   \n",
       "3555    -118.59    34.23                 17        6592           1525   \n",
       "\n",
       "      population households median_income ocean_proximity income_category  \\\n",
       "17606        710        339        2.7042       <1H OCEAN               2   \n",
       "18632        306        113        6.4214       <1H OCEAN               5   \n",
       "14650        936        462        2.8621      NEAR OCEAN               2   \n",
       "3230        1460        353        1.8839          INLAND               2   \n",
       "3555        4459       1463        3.0347       <1H OCEAN               3   \n",
       "\n",
       "      rooms_per_household population_per_household  \n",
       "17606             4.62537                   2.0944  \n",
       "18632             6.00885                  2.70796  \n",
       "14650             4.22511                  2.02597  \n",
       "3230              5.23229                  4.13598  \n",
       "3555              4.50581                  3.04785  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_extra_attribs = pd.DataFrame(\n",
    "    housing_extra_attribs,\n",
    "    columns=list(housing.columns)+[\"rooms_per_household\", \"population_per_household\"],\n",
    "    index=housing.index)\n",
    "housing_extra_attribs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
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       "      <th>rooms_per_household</th>\n",
       "      <th>population_per_household</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>12347</td>\n",
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       "      <td>INLAND</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>6263</td>\n",
       "      <td>-117.96</td>\n",
       "      <td>34.04</td>\n",
       "      <td>34</td>\n",
       "      <td>1381</td>\n",
       "      <td>265</td>\n",
       "      <td>1020</td>\n",
       "      <td>268</td>\n",
       "      <td>4.025</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>5.15299</td>\n",
       "      <td>3.80597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12208</td>\n",
       "      <td>-117.1</td>\n",
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       "      <td>6</td>\n",
       "      <td>1868</td>\n",
       "      <td>289</td>\n",
       "      <td>750</td>\n",
       "      <td>247</td>\n",
       "      <td>4.3833</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>7.56275</td>\n",
       "      <td>3.03644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6396</td>\n",
       "      <td>-118.03</td>\n",
       "      <td>34.14</td>\n",
       "      <td>44</td>\n",
       "      <td>1446</td>\n",
       "      <td>250</td>\n",
       "      <td>721</td>\n",
       "      <td>243</td>\n",
       "      <td>4.7308</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>4</td>\n",
       "      <td>5.95062</td>\n",
       "      <td>2.96708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12601</td>\n",
       "      <td>-121.48</td>\n",
       "      <td>38.53</td>\n",
       "      <td>37</td>\n",
       "      <td>1704</td>\n",
       "      <td>361</td>\n",
       "      <td>902</td>\n",
       "      <td>356</td>\n",
       "      <td>1.9837</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>4.78652</td>\n",
       "      <td>2.53371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13354</td>\n",
       "      <td>-117.61</td>\n",
       "      <td>34.02</td>\n",
       "      <td>15</td>\n",
       "      <td>1791</td>\n",
       "      <td>346</td>\n",
       "      <td>1219</td>\n",
       "      <td>328</td>\n",
       "      <td>3.8125</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "      <td>5.46037</td>\n",
       "      <td>3.71646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5749</td>\n",
       "      <td>-118.27</td>\n",
       "      <td>34.16</td>\n",
       "      <td>45</td>\n",
       "      <td>1865</td>\n",
       "      <td>360</td>\n",
       "      <td>973</td>\n",
       "      <td>349</td>\n",
       "      <td>3.6587</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>5.34384</td>\n",
       "      <td>2.78797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18799</td>\n",
       "      <td>-121.89</td>\n",
       "      <td>40.97</td>\n",
       "      <td>26</td>\n",
       "      <td>1183</td>\n",
       "      <td>276</td>\n",
       "      <td>513</td>\n",
       "      <td>206</td>\n",
       "      <td>2.225</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>5.74272</td>\n",
       "      <td>2.49029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15022</td>\n",
       "      <td>-117</td>\n",
       "      <td>32.77</td>\n",
       "      <td>30</td>\n",
       "      <td>1802</td>\n",
       "      <td>401</td>\n",
       "      <td>776</td>\n",
       "      <td>386</td>\n",
       "      <td>2.8125</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>4.66839</td>\n",
       "      <td>2.01036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16834</td>\n",
       "      <td>-122.55</td>\n",
       "      <td>37.59</td>\n",
       "      <td>31</td>\n",
       "      <td>1331</td>\n",
       "      <td>245</td>\n",
       "      <td>598</td>\n",
       "      <td>225</td>\n",
       "      <td>4.1827</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>5.91556</td>\n",
       "      <td>2.65778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1468</td>\n",
       "      <td>-121.99</td>\n",
       "      <td>37.96</td>\n",
       "      <td>17</td>\n",
       "      <td>2756</td>\n",
       "      <td>423</td>\n",
       "      <td>1228</td>\n",
       "      <td>426</td>\n",
       "      <td>5.5872</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>4</td>\n",
       "      <td>6.46948</td>\n",
       "      <td>2.88263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14906</td>\n",
       "      <td>-117.06</td>\n",
       "      <td>32.6</td>\n",
       "      <td>33</td>\n",
       "      <td>905</td>\n",
       "      <td>205</td>\n",
       "      <td>989</td>\n",
       "      <td>222</td>\n",
       "      <td>2.7014</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>4.07658</td>\n",
       "      <td>4.45495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10779</td>\n",
       "      <td>-117.91</td>\n",
       "      <td>33.65</td>\n",
       "      <td>17</td>\n",
       "      <td>1328</td>\n",
       "      <td>377</td>\n",
       "      <td>762</td>\n",
       "      <td>344</td>\n",
       "      <td>2.2222</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>3.86047</td>\n",
       "      <td>2.21512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7934</td>\n",
       "      <td>-118.08</td>\n",
       "      <td>33.82</td>\n",
       "      <td>26</td>\n",
       "      <td>4259</td>\n",
       "      <td>588</td>\n",
       "      <td>1644</td>\n",
       "      <td>581</td>\n",
       "      <td>6.2519</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "      <td>7.33046</td>\n",
       "      <td>2.8296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9745</td>\n",
       "      <td>-121.7</td>\n",
       "      <td>36.67</td>\n",
       "      <td>37</td>\n",
       "      <td>641</td>\n",
       "      <td>129</td>\n",
       "      <td>458</td>\n",
       "      <td>142</td>\n",
       "      <td>3.3456</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>4.51408</td>\n",
       "      <td>3.22535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18768</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>40.51</td>\n",
       "      <td>23</td>\n",
       "      <td>2216</td>\n",
       "      <td>378</td>\n",
       "      <td>1006</td>\n",
       "      <td>338</td>\n",
       "      <td>4.559</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>4</td>\n",
       "      <td>6.55621</td>\n",
       "      <td>2.97633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5564</td>\n",
       "      <td>-118.29</td>\n",
       "      <td>33.91</td>\n",
       "      <td>41</td>\n",
       "      <td>2475</td>\n",
       "      <td>532</td>\n",
       "      <td>1416</td>\n",
       "      <td>470</td>\n",
       "      <td>3.8372</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>5.26596</td>\n",
       "      <td>3.01277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7064</td>\n",
       "      <td>-118.03</td>\n",
       "      <td>33.94</td>\n",
       "      <td>30</td>\n",
       "      <td>2572</td>\n",
       "      <td>521</td>\n",
       "      <td>1564</td>\n",
       "      <td>501</td>\n",
       "      <td>3.4861</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>5.13373</td>\n",
       "      <td>3.12176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13637</td>\n",
       "      <td>-117.32</td>\n",
       "      <td>34.08</td>\n",
       "      <td>41</td>\n",
       "      <td>1359</td>\n",
       "      <td>264</td>\n",
       "      <td>786</td>\n",
       "      <td>244</td>\n",
       "      <td>2.5208</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>5.56967</td>\n",
       "      <td>3.22131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4827</td>\n",
       "      <td>-118.32</td>\n",
       "      <td>34.03</td>\n",
       "      <td>31</td>\n",
       "      <td>2206</td>\n",
       "      <td>501</td>\n",
       "      <td>1194</td>\n",
       "      <td>435</td>\n",
       "      <td>1.9531</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>5.07126</td>\n",
       "      <td>2.74483</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      longitude latitude housing_median_age total_rooms total_bedrooms  \\\n",
       "12347   -116.54    33.82                 12        9482           2501   \n",
       "6263    -117.96    34.04                 34        1381            265   \n",
       "12208    -117.1    33.56                  6        1868            289   \n",
       "6396    -118.03    34.14                 44        1446            250   \n",
       "12601   -121.48    38.53                 37        1704            361   \n",
       "13354   -117.61    34.02                 15        1791            346   \n",
       "5749    -118.27    34.16                 45        1865            360   \n",
       "18799   -121.89    40.97                 26        1183            276   \n",
       "15022      -117    32.77                 30        1802            401   \n",
       "16834   -122.55    37.59                 31        1331            245   \n",
       "1468    -121.99    37.96                 17        2756            423   \n",
       "14906   -117.06     32.6                 33         905            205   \n",
       "10779   -117.91    33.65                 17        1328            377   \n",
       "7934    -118.08    33.82                 26        4259            588   \n",
       "9745     -121.7    36.67                 37         641            129   \n",
       "18768   -122.24    40.51                 23        2216            378   \n",
       "5564    -118.29    33.91                 41        2475            532   \n",
       "7064    -118.03    33.94                 30        2572            521   \n",
       "13637   -117.32    34.08                 41        1359            264   \n",
       "4827    -118.32    34.03                 31        2206            501   \n",
       "\n",
       "      population households median_income ocean_proximity income_category  \\\n",
       "12347       2725       1300        1.5595          INLAND               2   \n",
       "6263        1020        268         4.025       <1H OCEAN               3   \n",
       "12208        750        247        4.3833       <1H OCEAN               3   \n",
       "6396         721        243        4.7308          INLAND               4   \n",
       "12601        902        356        1.9837          INLAND               2   \n",
       "13354       1219        328        3.8125          INLAND               3   \n",
       "5749         973        349        3.6587       <1H OCEAN               3   \n",
       "18799        513        206         2.225          INLAND               2   \n",
       "15022        776        386        2.8125       <1H OCEAN               2   \n",
       "16834        598        225        4.1827      NEAR OCEAN               3   \n",
       "1468        1228        426        5.5872          INLAND               4   \n",
       "14906        989        222        2.7014      NEAR OCEAN               2   \n",
       "10779        762        344        2.2222       <1H OCEAN               2   \n",
       "7934        1644        581        6.2519       <1H OCEAN               5   \n",
       "9745         458        142        3.3456       <1H OCEAN               3   \n",
       "18768       1006        338         4.559          INLAND               4   \n",
       "5564        1416        470        3.8372       <1H OCEAN               3   \n",
       "7064        1564        501        3.4861       <1H OCEAN               3   \n",
       "13637        786        244        2.5208          INLAND               2   \n",
       "4827        1194        435        1.9531       <1H OCEAN               2   \n",
       "\n",
       "      rooms_per_household population_per_household  \n",
       "12347             7.29385                  2.09615  \n",
       "6263              5.15299                  3.80597  \n",
       "12208             7.56275                  3.03644  \n",
       "6396              5.95062                  2.96708  \n",
       "12601             4.78652                  2.53371  \n",
       "13354             5.46037                  3.71646  \n",
       "5749              5.34384                  2.78797  \n",
       "18799             5.74272                  2.49029  \n",
       "15022             4.66839                  2.01036  \n",
       "16834             5.91556                  2.65778  \n",
       "1468              6.46948                  2.88263  \n",
       "14906             4.07658                  4.45495  \n",
       "10779             3.86047                  2.21512  \n",
       "7934              7.33046                   2.8296  \n",
       "9745              4.51408                  3.22535  \n",
       "18768             6.55621                  2.97633  \n",
       "5564              5.26596                  3.01277  \n",
       "7064              5.13373                  3.12176  \n",
       "13637             5.56967                  3.22131  \n",
       "4827              5.07126                  2.74483  "
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_extra_attribs.sample(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征缩放\n",
    "### 最重要也是最需要应用在数据上的转换器，就是特征缩放，输入数值属性有很大的比例差异，会导致机器学习算法性能表现不佳\n",
    "### 房间总数 范围6到39320，收入中位数范围0到15\n",
    "### 目标值通常不需要缩放\n",
    "### 同比缩放所有属性，2种方法 最小-最大缩放和标准化\n",
    "### 最小-最大缩放，又叫归一化，将值重新缩放到0到1之间，将值减去最小值并除以最大值和最小值差，如果你不希望是0到1，可以调整超参数feature_range\n",
    "### 标准化 减去平均值 ，所以标准化均值总是0，然后除以方差，结果的分布具备单位方差，不同于归一化，标准化不将值绑定到特定范围，受异常值影响小\n",
    "### 缩放器仅用来拟合训练集，不是完成的数据集\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 转换流水线\n",
    "### 许多数据转换的步骤需要以正确的顺序来执行， PipeLine来支持这样的转换\n",
    "### pipline构造函数会通过一系列名称/估算器的配对来定义步骤的序列，必须是转换器，必须有fit_fransform()方法\n",
    "### 调用流水线的fit方法时，会在所有转换器上按照顺序依次调用fit_transform()，将一个调用的输出作为参数传递给下一个调用方法，直到传递到最终\n",
    "### 估算器，只会调用fit方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "num_pipeline = Pipeline([\n",
    "        ('imputer', SimpleImputer(strategy=\"median\")),\n",
    "        ('attribs_adder', CombinedAttributesAdder()),\n",
    "        ('std_scaler', StandardScaler())\n",
    "    ])\n",
    "\n",
    "housing_num_tr = num_pipeline.fit_transform(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16512 entries, 17606 to 15775\n",
      "Data columns (total 9 columns):\n",
      "longitude             16512 non-null float64\n",
      "latitude              16512 non-null float64\n",
      "housing_median_age    16512 non-null float64\n",
      "total_rooms           16512 non-null float64\n",
      "total_bedrooms        16354 non-null float64\n",
      "population            16512 non-null float64\n",
      "households            16512 non-null float64\n",
      "median_income         16512 non-null float64\n",
      "income_category       16512 non-null category\n",
      "dtypes: category(1), float64(8)\n",
      "memory usage: 1.1 MB\n"
     ]
    }
   ],
   "source": [
    "housing_num.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dataFrame-->series-->ndarray\n",
    "class DataFrameSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, attribute_names):\n",
    "        self.attribute_names = attribute_names\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X):\n",
    "        return X[self.attribute_names].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-121.89, 37.29, 38.0, ..., 339.0, 2.7042, 2],\n",
       "       [-121.93, 37.05, 14.0, ..., 113.0, 6.4214, 5],\n",
       "       [-117.2, 32.77, 31.0, ..., 462.0, 2.8621, 2],\n",
       "       ...,\n",
       "       [-116.4, 34.09, 9.0, ..., 765.0, 3.2723, 3],\n",
       "       [-118.01, 33.82, 31.0, ..., 356.0, 4.0625, 3],\n",
       "       [-122.45, 37.77, 52.0, ..., 639.0, 3.575, 3]], dtype=object)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_attribs=list(housing_num)\n",
    "housing[num_attribs].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_pipeline = Pipeline([\n",
    "        ('selector', DataFrameSelector(num_attribs)),\n",
    "        ('imputer', SimpleImputer(strategy=\"median\")),\n",
    "        ('attribs_adder', CombinedAttributesAdder()),\n",
    "        ('std_scaler', StandardScaler()),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import TransformerMixin \n",
    "class MyLabelBinarizer(TransformerMixin):\n",
    "    def __init__(self, *args, **kwargs):\n",
    "        self.encoder = LabelBinarizer(*args, **kwargs)\n",
    "    def fit(self, x, y=0):\n",
    "        self.encoder.fit(x)\n",
    "        return self\n",
    "    def transform(self, x, y=0):\n",
    "        return self.encoder.transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>17606</td>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18632</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14650</td>\n",
       "      <td>-117.20</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3230</td>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3555</td>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
       "18632    -121.93     37.05                14.0        679.0           108.0   \n",
       "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
       "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
       "\n",
       "       population  households  median_income ocean_proximity income_category  \n",
       "17606       710.0       339.0         2.7042       <1H OCEAN               2  \n",
       "18632       306.0       113.0         6.4214       <1H OCEAN               5  \n",
       "14650       936.0       462.0         2.8621      NEAR OCEAN               2  \n",
       "3230       1460.0       353.0         1.8839          INLAND               2  \n",
       "3555       4459.0      1463.0         3.0347       <1H OCEAN               3  "
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_attribs = ['ocean_proximity']\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "\n",
    "cat_pipeline = Pipeline([\n",
    "        ('selector', DataFrameSelector(cat_attribs)),               \n",
    "        ('LabelBinarizer', MyLabelBinarizer()),\n",
    "    ])\n",
    "\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import FeatureUnion\n",
    "\n",
    "full_pipeline = FeatureUnion(transformer_list=[\n",
    "        (\"num_pipline\", num_pipeline,),\n",
    "        ('cat_pipline', cat_pipeline),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.15604281,  0.77194962,  0.74333089, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-1.17602483,  0.6596948 , -1.1653172 , ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 1.18684903, -1.34218285,  0.18664186, ...,  0.        ,\n",
       "         0.        ,  1.        ],\n",
       "       ...,\n",
       "       [ 1.58648943, -0.72478134, -1.56295222, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 0.78221312, -0.85106801,  0.18664186, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-1.43579109,  0.99645926,  1.85670895, ...,  0.        ,\n",
       "         1.        ,  0.        ]])"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_finished=full_pipeline.fit_transform(housing)\n",
    "housing_finished"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_prepared=pd.DataFrame(housing_finished)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-1.156043</td>\n",
       "      <td>0.771950</td>\n",
       "      <td>0.743331</td>\n",
       "      <td>-0.493234</td>\n",
       "      <td>-0.445438</td>\n",
       "      <td>-0.636211</td>\n",
       "      <td>-0.420698</td>\n",
       "      <td>-0.614937</td>\n",
       "      <td>-0.954456</td>\n",
       "      <td>-0.312055</td>\n",
       "      <td>-0.086499</td>\n",
       "      <td>0.155318</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-1.176025</td>\n",
       "      <td>0.659695</td>\n",
       "      <td>-1.165317</td>\n",
       "      <td>-0.908967</td>\n",
       "      <td>-1.036928</td>\n",
       "      <td>-0.998331</td>\n",
       "      <td>-1.022227</td>\n",
       "      <td>1.336459</td>\n",
       "      <td>1.890305</td>\n",
       "      <td>0.217683</td>\n",
       "      <td>-0.033534</td>\n",
       "      <td>-0.836289</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.186849</td>\n",
       "      <td>-1.342183</td>\n",
       "      <td>0.186642</td>\n",
       "      <td>-0.313660</td>\n",
       "      <td>-0.153345</td>\n",
       "      <td>-0.433639</td>\n",
       "      <td>-0.093318</td>\n",
       "      <td>-0.532046</td>\n",
       "      <td>-0.954456</td>\n",
       "      <td>-0.465315</td>\n",
       "      <td>-0.092405</td>\n",
       "      <td>0.422200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-0.017068</td>\n",
       "      <td>0.313576</td>\n",
       "      <td>-0.290520</td>\n",
       "      <td>-0.362762</td>\n",
       "      <td>-0.396756</td>\n",
       "      <td>0.036041</td>\n",
       "      <td>-0.383436</td>\n",
       "      <td>-1.045566</td>\n",
       "      <td>-0.954456</td>\n",
       "      <td>-0.079661</td>\n",
       "      <td>0.089736</td>\n",
       "      <td>-0.196453</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.492474</td>\n",
       "      <td>-0.659299</td>\n",
       "      <td>-0.926736</td>\n",
       "      <td>1.856193</td>\n",
       "      <td>2.412211</td>\n",
       "      <td>2.724154</td>\n",
       "      <td>2.570975</td>\n",
       "      <td>-0.441437</td>\n",
       "      <td>-0.006202</td>\n",
       "      <td>-0.357834</td>\n",
       "      <td>-0.004194</td>\n",
       "      <td>0.269928</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6  \\\n",
       "0 -1.156043  0.771950  0.743331 -0.493234 -0.445438 -0.636211 -0.420698   \n",
       "1 -1.176025  0.659695 -1.165317 -0.908967 -1.036928 -0.998331 -1.022227   \n",
       "2  1.186849 -1.342183  0.186642 -0.313660 -0.153345 -0.433639 -0.093318   \n",
       "3 -0.017068  0.313576 -0.290520 -0.362762 -0.396756  0.036041 -0.383436   \n",
       "4  0.492474 -0.659299 -0.926736  1.856193  2.412211  2.724154  2.570975   \n",
       "\n",
       "          7         8         9        10        11   12   13   14   15   16  \n",
       "0 -0.614937 -0.954456 -0.312055 -0.086499  0.155318  1.0  0.0  0.0  0.0  0.0  \n",
       "1  1.336459  1.890305  0.217683 -0.033534 -0.836289  1.0  0.0  0.0  0.0  0.0  \n",
       "2 -0.532046 -0.954456 -0.465315 -0.092405  0.422200  0.0  0.0  0.0  0.0  1.0  \n",
       "3 -1.045566 -0.954456 -0.079661  0.089736 -0.196453  0.0  1.0  0.0  0.0  0.0  \n",
       "4 -0.441437 -0.006202 -0.357834 -0.004194  0.269928  1.0  0.0  0.0  0.0  0.0  "
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 选择和训练模型\n",
    "* 获得了数据\n",
    "* 数据探索\n",
    "* 对训练集和测试集进行拆分\n",
    "* 编写了转换数据流水线\n",
    "* 自动清理和准备机器学习算法的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17606    286600.0\n",
       "18632    340600.0\n",
       "14650    196900.0\n",
       "3230      46300.0\n",
       "3555     254500.0\n",
       "           ...   \n",
       "6563     240200.0\n",
       "12053    113000.0\n",
       "13908     97800.0\n",
       "11159    225900.0\n",
       "15775    500001.0\n",
       "Name: median_house_value, Length: 16512, dtype: float64"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame(housing_prepared)\n",
    "df.head()\n",
    "housing_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 训练模型和评估训练集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#线性模型\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "#预测数据\n",
    "some_data=housing.iloc[:5]\n",
    "some_labels=housing_labels.iloc[:5]\n",
    "some_data_prepared=full_pipeline.transform(some_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测值: [203682.37379543 326371.39370781 204218.64588245 ...  98401.39527017\n",
      " 212187.94436578 278353.72728836]\n"
     ]
    }
   ],
   "source": [
    "housing_predictions = lin_reg.predict(housing_prepared)\n",
    "print(\"预测值:\", housing_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68376.64295459937"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error #均方根误差\n",
    "\n",
    "lin_mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "lin_mse\n",
    "lin_rmse = np.sqrt(lin_mse)\n",
    "lin_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# 大多数地区的房屋中位数 在120000到265000美元之间，预测误差高达 68628,这是一个典型的模型对训练数据拟合不足的案例，\n",
    "* 原因可能是特征无法提供足够的信息来做出更好的预测，或者模型本身不够强大，\n",
    "* 1. 选择强大的模型，2 为算法提供更好的特征，3.减少对模型的限制等方法，\n",
    "\n",
    "* 决策树可以找到复杂的非线性关系"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 标准差与均方根误差的区别：\n",
    "* 标准差是用来衡量一组数自身的离散程度，而均方根误差是用来衡量观测值同真值之间的偏差，它们的研究对象和研究目的不同\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# 标准差与均方根误差的区别：\n",
    "# 标准差是用来衡量一组数自身的离散程度，而均方根误差是用来衡量观测值同真值之间的偏差，它们的研究对象和研究目的不同\n",
    "\n",
    "\n",
    "# MAE是一种线性分数，所有个体差异在平均值上的权重都相等，比如，10和0之间的绝对误差是5和0之间绝对误差的两倍。但这对于RMSE而言不一样，后续将进一步详细讨论。\n",
    "# MAE很容易理解，因为它就是对残差直接计算平均，而RMSE相比MAE，会对高的差异惩罚更多\n",
    "\n",
    "\n",
    "# 案例1：真实值= [2,4,6,8]，预测值= [4,6,8,10]\n",
    "# 案例2：真实值= [2,4,6,8]，预测值= [4,6,8,12]\n",
    "# 案例1的MAE = 2.0，RMSE = 2.0\n",
    "# 案例2的MAE = 2.5，RMSE = 2.65\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 使用决策树算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(random_state=42)"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "tree_reg = DecisionTreeRegressor(random_state=42)\n",
    "tree_reg.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_predictions = tree_reg.predict(housing_prepared)\n",
    "tree_mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "tree_rmse = np.sqrt(tree_mse)\n",
    "tree_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "#也许是过度拟合了，可以使用交叉验证的方法来验证"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 使用train_test_split函数将训练集分为较小的训练集和验证集，然后根据这些较小的训练集来训练模型，并对其进行评估\n",
    "## sklearn的交叉验证，将训练集随机分割成10个不同的子集，每个子集称为一个折叠，对模型进行10次训练和评估，每次挑选1个折叠进行评估，另外9个进行训练\n",
    "## neg_mean_squared_error‘ 也就是 均方差回归损失 该统计参数是预测数据和原始数据对应点误差的平方和的均值\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "scores=cross_val_score(tree_reg,housing_prepared,housing_labels,scoring=\"neg_mean_squared_error\",cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([70274.7991723 , 67258.3624668 , 71350.42593227, 68882.91340979,\n",
       "       70987.99296566, 74177.52037059, 70788.57311306, 70850.53018019,\n",
       "       76430.62239321, 70212.6471067 ])"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_rmse_scores=np.sqrt(-scores)\n",
    "tree_rmse_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [70274.7991723  67258.3624668  71350.42593227 68882.91340979\n",
      " 70987.99296566 74177.52037059 70788.57311306 70850.53018019\n",
      " 76430.62239321 70212.6471067 ]\n",
      "Mean: 71121.4387110585\n",
      "Standard deviation: 2434.3080046605132\n"
     ]
    }
   ],
   "source": [
    "def display_scores(scores):\n",
    "    print(\"Scores:\", scores)\n",
    "    print(\"Mean:\", scores.mean())\n",
    "    print(\"Standard deviation:\", scores.std())\n",
    "\n",
    "display_scores(tree_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [66877.52325028 66608.120256   70575.91118868 74179.94799352\n",
      " 67683.32205678 71103.16843468 64782.65896552 67711.29940352\n",
      " 71080.40484136 67687.6384546 ]\n",
      "Mean: 68828.99948449328\n",
      "Standard deviation: 2662.761570610345\n"
     ]
    }
   ],
   "source": [
    "# 线性 交叉验证\n",
    "lin_scores = cross_val_score(lin_reg, housing_prepared, housing_labels,\n",
    "                             scoring=\"neg_mean_squared_error\", cv=10)\n",
    "lin_rmse_scores = np.sqrt(-lin_scores)\n",
    "display_scores(lin_rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 随机森林"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(n_estimators=10, random_state=42)"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "forest_reg = RandomForestRegressor(n_estimators=10, random_state=42)\n",
    "forest_reg.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22107.0888400092"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_predictions = forest_reg.predict(housing_prepared)\n",
    "forest_mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "forest_rmse = np.sqrt(forest_mse)\n",
    "forest_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [51481.61843757 48867.18698326 53592.93924497 54917.35753217\n",
      " 50463.39403515 56776.52132037 51940.08691385 50521.84102811\n",
      " 55729.5024898  53136.5656865 ]\n",
      "Mean: 52742.701367175265\n",
      "Standard deviation: 2412.0829538615826\n"
     ]
    }
   ],
   "source": [
    "forest_scores = cross_val_score(forest_reg, housing_prepared, housing_labels,\n",
    "                                scoring=\"neg_mean_squared_error\", cv=10)\n",
    "forest_rmse_scores = np.sqrt(-forest_scores)\n",
    "display_scores(forest_rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型调参和网格搜索\n",
    "* 手动调整超参数，找到很好的组合很难\n",
    "* 使用GridSearchCV替你进行搜索，告诉它，进行试验的超参数是什么，和需要尝试的值，它会使用交叉验证评估所有超参数的可能组合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestRegressor(random_state=42),\n",
       "             param_grid=[{'max_features': [2, 4, 6, 8],\n",
       "                          'n_estimators': [3, 10, 30]},\n",
       "                         {'bootstrap': [False], 'max_features': [2, 3, 4],\n",
       "                          'n_estimators': [3, 10]}],\n",
       "             return_train_score=True, scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [\n",
    "    {'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]},\n",
    "    {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]},\n",
    "  ]\n",
    "\n",
    "forest_reg = RandomForestRegressor(random_state=42)\n",
    "grid_search = GridSearchCV(forest_reg, param_grid, cv=5,\n",
    "                           scoring='neg_mean_squared_error', return_train_score=True)\n",
    "grid_search.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 6, 'n_estimators': 30}"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_features=6, n_estimators=30, random_state=42)"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64246.880700613496 {'max_features': 2, 'n_estimators': 3}\n",
      "55869.85367924813 {'max_features': 2, 'n_estimators': 10}\n",
      "53472.1282558969 {'max_features': 2, 'n_estimators': 30}\n",
      "61376.36445082522 {'max_features': 4, 'n_estimators': 3}\n",
      "53846.329115303764 {'max_features': 4, 'n_estimators': 10}\n",
      "51270.1941502407 {'max_features': 4, 'n_estimators': 30}\n",
      "59860.61532587693 {'max_features': 6, 'n_estimators': 3}\n",
      "53114.42460001889 {'max_features': 6, 'n_estimators': 10}\n",
      "50811.43543872171 {'max_features': 6, 'n_estimators': 30}\n",
      "59220.31563298743 {'max_features': 8, 'n_estimators': 3}\n",
      "52884.78697544277 {'max_features': 8, 'n_estimators': 10}\n",
      "50944.39369116168 {'max_features': 8, 'n_estimators': 30}\n",
      "62805.52917192821 {'bootstrap': False, 'max_features': 2, 'n_estimators': 3}\n",
      "54462.1410888642 {'bootstrap': False, 'max_features': 2, 'n_estimators': 10}\n",
      "61117.32056104296 {'bootstrap': False, 'max_features': 3, 'n_estimators': 3}\n",
      "53022.992252269294 {'bootstrap': False, 'max_features': 3, 'n_estimators': 10}\n",
      "60234.58052562756 {'bootstrap': False, 'max_features': 4, 'n_estimators': 3}\n",
      "52712.989031117104 {'bootstrap': False, 'max_features': 4, 'n_estimators': 10}\n"
     ]
    }
   ],
   "source": [
    "cvres=grid_search.cv_results_\n",
    "for mean_scrore,params in zip(cvres[\"mean_test_score\"],cvres[\"params\"]):\n",
    "    print(np.sqrt(-mean_scrore),params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据准备步骤也可以当作超参数来处理，网格搜索会自动查找是否添加你不确定的特征，比如是否使用转换器 combinedAttre的超参数add_bedrooms_per_rom\n",
    "# 也可是使用它自动寻找处理问题的最佳方法，比如异常值，缺失特征，特征选择等"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# 随机搜索\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(random_state=42),\n",
       "                   param_distributions={'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x00000214E3458408>,\n",
       "                                        'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x00000214E3458C48>},\n",
       "                   random_state=42, scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint\n",
    "\n",
    "param_distribs = {\n",
    "        'n_estimators': randint(low=1, high=200),\n",
    "        'max_features': randint(low=0, high=5),\n",
    "    }\n",
    "\n",
    "forest_reg = RandomForestRegressor(random_state=42)\n",
    "rnd_search = RandomizedSearchCV(forest_reg, param_distributions=param_distribs,\n",
    "                                n_iter=10, cv=5, scoring='neg_mean_squared_error', random_state=42)\n",
    "rnd_search.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "51242.793542507105 {'max_features': 3, 'n_estimators': 93}\n",
      "52551.16635897673 {'max_features': 2, 'n_estimators': 72}\n",
      "51727.60899097628 {'max_features': 4, 'n_estimators': 21}\n",
      "54796.11313391796 {'max_features': 1, 'n_estimators': 75}\n",
      "52402.52976902194 {'max_features': 2, 'n_estimators': 88}\n",
      "50385.369810231794 {'max_features': 4, 'n_estimators': 100}\n",
      "52144.88833564394 {'max_features': 2, 'n_estimators': 150}\n",
      "65600.60191591198 {'max_features': 4, 'n_estimators': 2}\n",
      "50957.939217798084 {'max_features': 3, 'n_estimators': 158}\n",
      "54216.87001692321 {'max_features': 1, 'n_estimators': 192}\n"
     ]
    }
   ],
   "source": [
    "cvres = rnd_search.cv_results_\n",
    "for mean_score, params in zip(cvres[\"mean_test_score\"], cvres[\"params\"]):\n",
    "    print(np.sqrt(-mean_score), params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 分析最佳模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.79326113e-02, 6.18280724e-02, 4.33395023e-02, 1.81017027e-02,\n",
       "       1.83291556e-02, 1.93269892e-02, 1.78369580e-02, 2.41360490e-01,\n",
       "       1.61976585e-01, 5.35982558e-02, 1.06273526e-01, 6.14045141e-02,\n",
       "       1.22353255e-02, 1.08821239e-01, 2.76143239e-05, 2.59938294e-03,\n",
       "       5.00807682e-03])"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances = grid_search.best_estimator_.feature_importances_\n",
    "feature_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.24136048955382883, 'median_income'),\n",
       " (0.16197658459849276, 'income_category'),\n",
       " (0.10882123891274476, 'INLAND'),\n",
       " (0.10627352591969835, 'population_per_household'),\n",
       " (0.06793261134305181, 'longitude'),\n",
       " (0.06182807241916786, 'latitude'),\n",
       " (0.061404514078416045, 'bedrooms_per_room'),\n",
       " (0.05359825584988402, 'rooms_per_household'),\n",
       " (0.04333950231438806, 'housing_median_age'),\n",
       " (0.019326989179411204, 'population'),\n",
       " (0.01832915558242795, 'total_bedrooms'),\n",
       " (0.01810170268968371, 'total_rooms'),\n",
       " (0.01783695799011688, 'households'),\n",
       " (0.012235325483341324, '<1H OCEAN'),\n",
       " (0.0050080768169210735, 'NEAR OCEAN'),\n",
       " (0.002599382944523225, 'NEAR BAY'),\n",
       " (2.7614323902184926e-05, 'ISLAND')]"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# num_attribs\n",
    "extra_attribs = [\"rooms_per_household\", \"population_per_household\", \"bedrooms_per_room\"]\n",
    "cat_one_hot_attribs = list(encoder.classes_)\n",
    "attributes = num_attribs + extra_attribs + cat_one_hot_attribs\n",
    "sorted(zip(feature_importances, attributes), reverse = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 通过测试集评估系统\n",
    "* 从测试集中获取预测器和标签\n",
    "* 运行full_pipline来转换数据\n",
    "* 在测试集上评估最终模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model=grid_search.best_estimator_\n",
    "X_test=strat_test_set.drop('median_house_value',axis=1)\n",
    "y_test=strat_test_set['median_house_value'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_prepared=full_pipeline.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_predicttion=final_model.predict(X_test_prepared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49040.18609727207"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_mse=mean_squared_error(y_test,final_predicttion)\n",
    "final_rmse=np.sqrt(final_mse)\n",
    "final_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 项目启动阶段\n",
    "1. 展示解决方案 学习了什么\n",
    "2. 什么有用\n",
    "3. 什么没有用\n",
    "4. 基于什么假设\n",
    "5. 以及系统的限制有哪些\n",
    "6. 制作漂亮的演示文稿，例如收入中位数是预测房价的首要指标"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 启动，监控和维护系统\n",
    "1. 为生产环境做好准备，将生产数据源接入系统\n",
    "2. 编写监控代码，定期检查系统的实时性能，性能下降时触发警报，系统崩溃和性能退化\n",
    "3. 时间推移，模型会渐渐腐坏，定期使用新数据训练模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 评估系统性能\n",
    "1. 需要对系统的预测结果进行抽样评估，需要人工分析，分析师可能是专家，平台工作人员，都需要将人工评估的流水线接入你的系统\n",
    "2. 评估输入系统的数据质量\n",
    "3. 使用新鲜数据定期训练你的模型，最多6个月"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 总结\n",
    "## 本周主要学习 数据准备，构建监控工作，建立人工评估流水线，自动化定期训练模型上，熟悉整个机器学习流程，\n",
    "# 建议\n",
    "## 选择一个数据集，尝试从A到Z的整个过程，从竞赛网站上，选择一个数据集，一个明确目标，以及可以一起分享经验的同伴\n",
    "https://www.kaggle.com/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第二题\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 在快排算法中，有一个典型的操作：partition。这个操作指：根据一个数值x，把数组中的元素划分成两半，使得index前面的元素都不大于x，\n",
    "### index后面的元素都不小于x\n",
    "### 将数组中所有元素（包括重复元素）从小到大排列，比第k大的元素小的放在前面，大的放在后面，输出新数组索引,\n",
    "### arr为负数时候，就是降序排列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'(slice(None, 2, None), (1, 2))' is an invalid key",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-231-9877e3fc1fd0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mhousing\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32me:\\python3.7\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2978\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2979\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2980\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2981\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2982\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32me:\\python3.7\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2895\u001b[0m                 )\n\u001b[0;32m   2896\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2897\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2898\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2899\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: '(slice(None, 2, None), (1, 2))' is an invalid key"
     ]
    }
   ],
   "source": [
    "housing[:2,[]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 7 6 0 3 2 5 4]\n",
      "[2. 0. 1. 3. 4. 5. 6. 7.]\n",
      "[2. 0. 1. 3.]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "dists= np.array([ 3.0, 2.0, 5.0, 4.0, 7.0, 6.0, 1.0, 0.0])\n",
    "\n",
    "k = 5\n",
    "print(np.argpartition(dists, k))\n",
    "print(dists[np.argpartition(dists, k)])#输出新数组索引对应的数组\n",
    "\n",
    "# 取X个比k名次还高的\n",
    "X = 4\n",
    "print(dists[np.argpartition(dists, k)[:X]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 7, 6, 0, 3, 2, 5, 4], dtype=int64)"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_dist1= np.array([ 3.0, 2.0, 5.0, 4.0, 7.0, 6.0, 1.0, 0.0])\n",
    "np.argpartition(test_dist1, -4)\n",
    "# np.argpartition(test_dist1, -4)[-4:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['2.7614323902184926e-05' 'ISLAND']\n",
      " ['0.002599382944523225' 'NEAR BAY']\n",
      " ['0.0050080768169210735' 'NEAR OCEAN']\n",
      " ['0.012235325483341324' '<1H OCEAN']\n",
      " ['0.01783695799011688' 'households']\n",
      " ['0.01810170268968371' 'total_rooms']\n",
      " ['0.01832915558242795' 'total_bedrooms']\n",
      " ['0.019326989179411204' 'population']\n",
      " ['0.04333950231438806' 'housing_median_age']\n",
      " ['0.05359825584988402' 'rooms_per_household']\n",
      " ['0.061404514078416045' 'bedrooms_per_room']\n",
      " ['0.06182807241916786' 'latitude']\n",
      " ['0.06793261134305181' 'longitude']\n",
      " ['0.10627352591969835' 'population_per_household']\n",
      " ['0.10882123891274476' 'INLAND']\n",
      " ['0.16197658459849276' 'income_category']\n",
      " ['0.24136048955382883' 'median_income']]\n"
     ]
    }
   ],
   "source": [
    "# >>>a = [1,2,3]\n",
    "# >>> b = [4,5,6]\n",
    "# >>> c = [4,5,6,7,8]\n",
    "# >>> zipped = zip(a,b)     # 打包为元组的列表\n",
    "# [(1, 4), (2, 5), (3, 6)]\n",
    "# >>> zip(a,c)              # 元素个数与最短的列表一致\n",
    "# [(1, 4), (2, 5), (3, 6)]\n",
    "# >>> zip(*zipped)          # 与 zip 相反，*zipped 可理解为解压，返回二维矩阵式\n",
    "# [(1, 2, 3), (4, 5, 6)]\n",
    "\n",
    "\n",
    "ba =sorted(zip(feature_importances, attributes))\n",
    "print(np.array(ba))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3. 2. 5. 4. 7. 6. 1. 0.]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([2, 4, 5], dtype=int64)"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def indices_of_top_k(arr, k):\n",
    "    return np.sort(np.argpartition(np.array(arr), -k)[-k:])#找到数组最大的k个元素，结果是位置索引\n",
    "print(test_dist1)\n",
    "indices_of_top_k(test_dist1,3) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "class TopFeatureSelector(BaseEstimator, TransformerMixin):  #选取前k名最重要的特征\n",
    "    def __init__(self, feature_importances, k):\n",
    "        self.feature_importances = feature_importances\n",
    "        self.k = k\n",
    "    def fit(self, X, y = None):\n",
    "        self.feature_indices_ = indices_of_top_k(self.feature_importances, self.k) #选取前k重要的索引\n",
    "        return self\n",
    "    def transform(self, X):\n",
    "        return X[:, self.feature_indices_]#返回前k重要的索引的元素"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.79326113e-02, 6.18280724e-02, 4.33395023e-02, 1.81017027e-02,\n",
       "       1.83291556e-02, 1.93269892e-02, 1.78369580e-02, 2.41360490e-01,\n",
       "       1.61976585e-01, 5.35982558e-02, 1.06273526e-01, 6.14045141e-02,\n",
       "       1.22353255e-02, 1.08821239e-01, 2.76143239e-05, 2.59938294e-03,\n",
       "       5.00807682e-03])"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['longitude',\n",
       " 'latitude',\n",
       " 'housing_median_age',\n",
       " 'total_rooms',\n",
       " 'total_bedrooms',\n",
       " 'population',\n",
       " 'households',\n",
       " 'median_income',\n",
       " 'income_category',\n",
       " 'rooms_per_household',\n",
       " 'population_per_household',\n",
       " 'bedrooms_per_room',\n",
       " '<1H OCEAN',\n",
       " 'INLAND',\n",
       " 'ISLAND',\n",
       " 'NEAR BAY',\n",
       " 'NEAR OCEAN']"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  7,  8, 10, 13], dtype=int64)"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 5\n",
    "top_k_feature_indices = indices_of_top_k(feature_importances, k)#选取前5重要的特征的索引\n",
    "top_k_feature_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['longitude', 'median_income', 'income_category',\n",
       "       'population_per_household', 'INLAND'], dtype='<U24')"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(attributes)[top_k_feature_indices]#选取前5重要的特征的索引对应的数组元素"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['0.24136048955382883', 'median_income'],\n",
       "       ['0.16197658459849276', 'income_category'],\n",
       "       ['0.10882123891274476', 'INLAND'],\n",
       "       ['0.10627352591969835', 'population_per_household'],\n",
       "       ['0.06793261134305181', 'longitude']], dtype='<U32')"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bba = sorted(zip(feature_importances, attributes), reverse=True)[:k]#将feature_importances与attributes组合排序，选取前k个元素\n",
    "bba_np = np.array(bba)\n",
    "bba_np\n",
    "# bba_np[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "preparation_and_feature_selection_pipeline = Pipeline([\n",
    "    ('preparation', full_pipeline),#先使用full_pipline\n",
    "    ('feature_selection', TopFeatureSelector(feature_importances, k))#再对full_pipline结果进行前k重要的特征选取\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.15604281, -0.61493744, -0.95445595, -0.08649871,  0.        ],\n",
       "       [-1.17602483,  1.33645936,  1.89030518, -0.03353391,  0.        ],\n",
       "       [ 1.18684903, -0.5320456 , -0.95445595, -0.09240499,  0.        ]])"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared_top_k_features = preparation_and_feature_selection_pipeline.fit_transform(housing)#先fit再transform\n",
    "housing_prepared_top_k_features[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>median_income</th>\n",
       "      <th>income_category</th>\n",
       "      <th>INLAND</th>\n",
       "      <th>population_per_household</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-1.156043</td>\n",
       "      <td>-0.614937</td>\n",
       "      <td>-0.954456</td>\n",
       "      <td>-0.086499</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-1.176025</td>\n",
       "      <td>1.336459</td>\n",
       "      <td>1.890305</td>\n",
       "      <td>-0.033534</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.186849</td>\n",
       "      <td>-0.532046</td>\n",
       "      <td>-0.954456</td>\n",
       "      <td>-0.092405</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-0.017068</td>\n",
       "      <td>-1.045566</td>\n",
       "      <td>-0.954456</td>\n",
       "      <td>0.089736</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.492474</td>\n",
       "      <td>-0.441437</td>\n",
       "      <td>-0.006202</td>\n",
       "      <td>-0.004194</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   median_income  income_category    INLAND  population_per_household  \\\n",
       "0      -1.156043        -0.614937 -0.954456                 -0.086499   \n",
       "1      -1.176025         1.336459  1.890305                 -0.033534   \n",
       "2       1.186849        -0.532046 -0.954456                 -0.092405   \n",
       "3      -0.017068        -1.045566 -0.954456                  0.089736   \n",
       "4       0.492474        -0.441437 -0.006202                 -0.004194   \n",
       "\n",
       "   longitude  \n",
       "0        0.0  \n",
       "1        0.0  \n",
       "2        0.0  \n",
       "3        1.0  \n",
       "4        0.0  "
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared = pd.DataFrame(housing_prepared_top_k_features, columns = bba_np[:,1])#使用排序\n",
    "housing_prepared.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第三题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "prepare_select_and_predict_pipeline = Pipeline([\n",
    "    ('preparation', full_pipeline),#引入之前的pipeline\n",
    "    ('feature_selection', TopFeatureSelector(feature_importances, k)),#加入选择重要属性的转换器\n",
    "    ('final_model', RandomForestRegressor(**rnd_search.best_params_))#生成模型\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('preparation',\n",
       "                 FeatureUnion(transformer_list=[('num_pipline',\n",
       "                                                 Pipeline(steps=[('selector',\n",
       "                                                                  DataFrameSelector(attribute_names=['longitude',\n",
       "                                                                                                     'latitude',\n",
       "                                                                                                     'housing_median_age',\n",
       "                                                                                                     'total_rooms',\n",
       "                                                                                                     'total_bedrooms',\n",
       "                                                                                                     'population',\n",
       "                                                                                                     'households',\n",
       "                                                                                                     'median_income',\n",
       "                                                                                                     'income_category'])),\n",
       "                                                                 ('imputer',\n",
       "                                                                  SimpleImputer(strategy='median')),\n",
       "                                                                 ('attribs_adder',\n",
       "                                                                  CombinedAt...\n",
       "                 TopFeatureSelector(feature_importances=array([6.79326113e-02, 6.18280724e-02, 4.33395023e-02, 1.81017027e-02,\n",
       "       1.83291556e-02, 1.93269892e-02, 1.78369580e-02, 2.41360490e-01,\n",
       "       1.61976585e-01, 5.35982558e-02, 1.06273526e-01, 6.14045141e-02,\n",
       "       1.22353255e-02, 1.08821239e-01, 2.76143239e-05, 2.59938294e-03,\n",
       "       5.00807682e-03]),\n",
       "                                    k=5)),\n",
       "                ('final_model', RandomForestRegressor(max_features=4))])"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prepare_select_and_predict_pipeline.fit(housing, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测值:\t [262114.   336790.02 204006.    49930.  ]\n",
      "实际值:\t\t [286600.0, 340600.0, 196900.0, 46300.0]\n"
     ]
    }
   ],
   "source": [
    "# 从完整数据集中去前4个数据和标签, 数据用于完整\n",
    "some_data = housing.iloc[:4]\n",
    "some_labels = housing_labels.iloc[:4]\n",
    "\n",
    "print(\"预测值:\\t\", prepare_select_and_predict_pipeline.predict(some_data))\n",
    "print(\"实际值:\\t\\t\", list(some_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第四题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 2 candidates, totalling 10 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  10 out of  10 | elapsed:    9.1s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'preparation__num_pipline__attribs_adder__add_bedrooms_per_room': True}"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = [{\n",
    "      'preparation__num_pipline__attribs_adder__add_bedrooms_per_room':[True, False]\n",
    "}]\n",
    "\n",
    "grid_search_prep = GridSearchCV(prepare_select_and_predict_pipeline, param_grid, cv=5,\n",
    "                                scoring='neg_mean_squared_error', verbose=2, n_jobs=4)#GridSearch 搜索最佳参数组合，会去判断preparation__num_pipline__attribs_adder__add_bedrooms_per_room取值为True或者False\n",
    "grid_search_prep.fit(housing, housing_labels)\n",
    "grid_search_prep.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 使用SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "prepare_select_and_predict_pipeline = Pipeline([\n",
    "    ('preparation', full_pipeline),\n",
    "    ('feature_selection', TopFeatureSelector(feature_importances, k)),\n",
    "     ('svm_reg', SVR())\n",
    "    \n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('preparation',\n",
       "                 FeatureUnion(transformer_list=[('num_pipline',\n",
       "                                                 Pipeline(steps=[('selector',\n",
       "                                                                  DataFrameSelector(attribute_names=['longitude',\n",
       "                                                                                                     'latitude',\n",
       "                                                                                                     'housing_median_age',\n",
       "                                                                                                     'total_rooms',\n",
       "                                                                                                     'total_bedrooms',\n",
       "                                                                                                     'population',\n",
       "                                                                                                     'households',\n",
       "                                                                                                     'median_income',\n",
       "                                                                                                     'income_category'])),\n",
       "                                                                 ('imputer',\n",
       "                                                                  SimpleImputer(strategy='median')),\n",
       "                                                                 ('attribs_adder',\n",
       "                                                                  CombinedAt...\n",
       "                                                                  <__main__.MyLabelBinarizer object at 0x00000214E2786788>)]))])),\n",
       "                ('feature_selection',\n",
       "                 TopFeatureSelector(feature_importances=array([6.79326113e-02, 6.18280724e-02, 4.33395023e-02, 1.81017027e-02,\n",
       "       1.83291556e-02, 1.93269892e-02, 1.78369580e-02, 2.41360490e-01,\n",
       "       1.61976585e-01, 5.35982558e-02, 1.06273526e-01, 6.14045141e-02,\n",
       "       1.22353255e-02, 1.08821239e-01, 2.76143239e-05, 2.59938294e-03,\n",
       "       5.00807682e-03]),\n",
       "                                    k=5)),\n",
       "                ('svm_reg', SVR())])"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prepare_select_and_predict_pipeline.fit(housing, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测值:\t [262928.   340923.01 207592.    48869.  ]\n",
      "观测值:\t\t [286600.0, 340600.0, 196900.0, 46300.0]\n"
     ]
    }
   ],
   "source": [
    "some_data = housing.iloc[:4]\n",
    "some_labels = housing_labels.iloc[:4]\n",
    "\n",
    "print(\"预测值:\\t\", prepare_select_and_predict_pipeline.predict(some_data))\n",
    "print(\"观测值:\\t\\t\", list(some_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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